This repository contains the annotated collection of 507 papers included in the study: "A Decade of Knowledge Graphs in Natural Language Processing: A Survey", published in AACL-IJCNLP 2022. The paper is published in the ACL Anthology: https://aclanthology.org/2022.aacl-main.46. The full dataset is available as a CSV file in this repository.
For citing this study in academic papers, presentations, or theses, please use the following BibTeX entry:
@inproceedings{schneider-etal-2022-decade,
title = "A Decade of Knowledge Graphs in Natural Language Processing: A Survey",
author = "Schneider, Phillip and
Schopf, Tim and
Vladika, Juraj and
Galkin, Mikhail and
Simperl, Elena and
Matthes, Florian",
editor = "He, Yulan and
Ji, Heng and
Li, Sujian and
Liu, Yang and
Chang, Chua-Hui",
booktitle = "Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = nov,
year = "2022",
address = "Online only",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.aacl-main.46",
pages = "601--614",
abstract = "In pace with developments in the research field of artificial intelligence, knowledge graphs (KGs) have attracted a surge of interest from both academia and industry. As a representation of semantic relations between entities, KGs have proven to be particularly relevant for natural language processing (NLP), experiencing a rapid spread and wide adoption within recent years. Given the increasing amount of research work in this area, several KG-related approaches have been surveyed in the NLP research community. However, a comprehensive study that categorizes established topics and reviews the maturity of individual research streams remains absent to this day. Contributing to closing this gap, we systematically analyzed 507 papers from the literature on KGs in NLP. Our survey encompasses a multifaceted review of tasks, research types, and contributions. As a result, we present a structured overview of the research landscape, provide a taxonomy of tasks, summarize our findings, and highlight directions for future work.",
}
| Document Type | Title | Keywords | Abstract (Truncated) | Source Database | Year | DOI | Authors | URL | Affiliated Countries | Tasks | Research Type | Contribution Type | Domain |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Conference Paper | Cross-Lingual Link Discovery between Chinese and English Wiki Knowledge Bases | Computational linguistics; Anchor strengths; Chinese documents; Critical issues; Hybrid approach; Knowledge basis; Knowledge graphs; Link Discovery; Topic relevance; Data mining(...) | Wikipedia is an online multilingual encyclopedia that contains a very large number of articles covering most written languages. However, one critical issue for Wikipedia is that the pages in different languages are rarely linked except for the cross-lingual link between pages about the same subject. This could pose serious difficulties to humans and machines who try to seek information from different lingual sources. In order to address above issue, we propose a hybrid approach that exploits anc(...) | ACL | 2013 | - | Miao Q., Lu H., Zhang S., Meng Y. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84922783862&partnerID=40&md5=f6f8378f947a26f10f4ad4f6f598c9ba | China | entity alignment | validation research | method | - |
| Conference Paper | Gem-Based Entity-Knowledge Maintenance | Emerging entities; Knowledge acceleration; Knowledge maintenance; Long-tail entities; Novelty; Relatedness(...) | Knowledge bases about entities have become a vital asset for Web search, recommendations, and analytics. Examples are Freebase being the core of the Google Knowledge Graph and the use of Wikipedia for distant supervision in numerous IR and NLP tasks. However, maintaining the knowledge about not so prominent entities in the long tail is often a bottleneck as human contributors face the tedious task of continuously identifying and reading relevant sources. To overcome this limitation and accelerat(...) | ACM | 2013 | 10.1145/2505515.2505715 | Taneva B., Weikum G. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84889592632&doi=10.1145%2f2505515.2505715&partnerID=40&md5=3c39903eabd16c6071f97c98070a1426 | Germany | semantic search | validation research | method | - |
| Journal Article | Graphical Induction of Qualified Medical Knowledge | Bioinformatics; electronic medical record; information retrieval; knowledge extraction(...) | The introduction of electronic medical records (EMRs) enabled the access of unprecedented volumes of clinical data, both in structured and unstructured formats. A significant amount of this clinical data is expressed within the narrative portion of the EMRs, requiring natural language processing techniques to unlock the medical knowledge referred to by physicians. This knowledge, derived from the practice of medical care, complements medical knowledge already encoded in various structured biomed(...) | Scopus | 2013 | 10.1142/s1793351x13400126 | Goodwin T., Harabagiu S.M. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84996542297&doi=10.1142%2fS1793351X13400126&partnerID=40&md5=999debbd9d84ce8c577974dd6da9284e | United States | entity extraction, relation extraction | solution proposal | method | health |
| Conference Paper | Building Sentiment Lexicons for All Major Languages | Sentiment analysis; Component language; Cultural difference; Knowledge graphs; Language pairs; Linguistic resources; Scarce resources; Sentiment lexicons; Wikipedia articles; Computational linguistics(...) | Sentiment analysis in a multilingual world remains a challenging problem, because developing language-specific sentiment lexicons is an extremely resourceintensive process. Such lexicons remain a scarce resource for most languages. In this paper, we address this lexicon gap by building high-quality sentiment lexicons for 136 major languages. We integrate a variety of linguistic resources to produce an immense knowledge graph. By appropriately propagating from seed words, we construct sentiment l(...) | ACL | 2014 | 10.3115/v1/p14-2063 | Chen Y., Skiena S. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84906927782&doi=10.3115%2fv1%2fp14-2063&partnerID=40&md5=21feca95c076bbfb9e61213e4383cbf7 | United States | text analysis | validation research | resource | - |
| Conference Paper | From Natural Language to Ontology Population in the Cultural Heritage Domain a Computational Linguistics-Based Approach | - | This paper presents an on-going Natural Language Processing (NLP) research based on Lexicon-Grammar (LG) and aimed at improving knowledge management of Cultural Heritage (CH) domain. We intend to demonstrate how our language formalization technique can be applied for both processing and populating a domain ontology. We also use NLP techniques for text extraction and mining to fill information gaps and improve access to cultural resources. The Linguistic Resources (LRs, i.e. electronic dictionari(...) | ACL | 2014 | - | di Buono, Maria Pia and Monteleone, Mario | http://www.lrec-conf.org/proceedings/lrec2014/pdf/686_Paper.pdf | Italy | entity extraction, relation extraction | solution proposal | method | culture |
| Conference Paper | Knowledge Graph and Text Jointly Embedding | Linguistics; Natural language processing systems; Vector spaces; Analogical reasoning; Embedding method; Embedding process; Knowledge graphs; Large scale experiments; Text corpora; Wikipedia; Embeddings(...) | We examine the embedding approach to reason new relational facts from a largescale knowledge graph and a text corpus. We propose a novel method of jointly embedding entities and words into the same continuous vector space. The embedding process attempts to preserve the relations between entities in the knowledge graph and the concurrences of words in the text corpus. Entity names and Wikipedia anchors are utilized to align the embeddings of entities and words in the same space. Large scale exper(...) | ACL | 2014 | 10.3115/v1/d14-1167 | Wang Z., Zhang J., Feng J., Chen Z. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84926065966&doi=10.3115%2fv1%2fd14-1167&partnerID=40&md5=eb21d9b18f85533cd6ff04cbef47a079 | China, United States | entity classification, link prediction | validation research | technique | - |
| Conference Paper | Ontology-Based Translation of Natural Language Queries to Sparql | Data storage equipment; Digital storage; Knowledge representation; Natural language processing systems; Query processing; Architectural approach; Back-ground knowledge; Effective approaches; Knowledge graphs; Natural language queries; Natural languages; Ontology-based; Query efficiency; Big data(...) | We present an implemented approach to transform natur al language sentences into SPARQL. using background knowledge from ontologies and lexicons. Therefore, eli gible technologies and data storage possibilities are ana lyzed and evaluated. The contributions of this paper are twofold. Firstly, we describe the motivation and current needs for a natural language access to industry data. We describe several scenarios where the proposed solution is required. Resulting in an architectural approach bas(...) | Scopus | 2014 | - | Sander M., Waltinger U., Roshchin M., Runkler T. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84987648006&partnerID=40&md5=b1132f2375f92b2c6dd085930ba8309a | Germany | machine translation | evaluation research | method | - |
| Conference Paper | Rc-Net: a General Framework for Incorporating Knowledge into Word Representations | Deep learning; Distributed word representations; Knowledge graph(...) | Representing words into vectors in continuous space can form up a potentially powerful basis to generate high-quality textual features for many text mining and natural language processing tasks. Some recent efforts, such as the skip-gram model, have attempted to learn word representations that can capture both syntactic and semantic information among text corpus. However, they still lack the capability of encoding the properties of words and the complex relationships among words very well, since(...) | ACM | 2014 | 10.1145/2661829.2662038 | Xu C., Bai Y., Bian J., Gao B., Wang G., Liu X., Liu T.-Y. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84937567033&doi=10.1145%2f2661829.2662038&partnerID=40&md5=b6fdb47bf7af72dfb02f96491b291832 | China | natural language inference, text classification | validation research | technique | - |
| Conference Paper | Tailor Knowledge Graph for Query Understanding: Linking Intent Topics by Propagation | Graphic methods; Natural language processing systems; Global knowledge; Knowledge graphs; Local contexts; Query logs; Query representations; Unsupervised algorithms; Information retrieval(...) | Knowledge graphs are recently used for enriching query representations in an entity-aware way for the rich facts organized around entities in it. However, few of the methods pay attention to non-entity words and clicked websites in queries, which also help conveying user intent. In this paper, we tackle the problem of intent understanding with innovatively representing entity words, refiners and clicked urls as intent topics in a unified knowledge graph based framework, in a way to exploit and e(...) | ACL | 2014 | 10.3115/v1/d14-1114 | Zhao S., Zhang Y. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84925987420&doi=10.3115%2fv1%2fd14-1114&partnerID=40&md5=ca0b878ae63563f4b2c9f55f55cea827 | China | semantic search | validation research | technique | - |
| Conference Paper | The Wisdom of Minority: Unsupervised Slot Filling Validation Based on Multi-Dimensional Truth-Finding | Computational linguistics; Information sources; Knowledge graphs; Linguistic analysis; Multi dimensional; Multi-source system; Multiple source; Multiple systems; Supervised methods; Linguistics(...) | Information Extraction using multiple information sources and systems is beneficial due to multisource/ system consolidation and challenging due to the resulting inconsistency and redundancy. We integrate IE and truth-finding research and present a novel unsupervised multi-dimensional truth finding framework which incorporates signals from multiple sources, multiple systems and multiple pieces of evidence by knowledge graph construction through multi-layer deep linguistic analysis. Experiments o(...) | ACL | 2014 | - | Yu D., Huang H., Cassidy T., Ji H., Wang C., Zhi S., Han J., Voss C., Magdon-Ismail M. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84959925878&partnerID=40&md5=90f28cb8be63c97f482b727a68d32de7 | United States | text analysis | validation research | method | - |
| Conference Paper | A Natural Language Interface for Search and Recommendations of Digital Entertainment Media | Digital storage; Human computer interaction; Search engines; Conversation interface; Digital entertainment; Integrated platform; Knowledge graphs; Named entity recognition; Natural language interfaces; Natural languages; Relationships between entities; Natural language processing systems(...) | We describe an integrated platform that combines a search and recommendations system of digital media with a novel conversation interface that enables users to use natural-language conversation for performing a variety of tasks on the digital content and information retrieval relating to meta-content. This advanced platform is built over a knowledge graph that consists of millions of tagged entities, along with structured relationships and popularities crawled and ingested from multiple sources,(...) | Scopus | 2015 | - | Venkataraman S., Mohaideen N.A. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85048175643&partnerID=40&md5=57c0505be7679b94f767aa3e59d48b3c | United States | conversational interfaces, semantic search | solution proposal | method | entertainment media |
| Conference Paper | An Entity-Centric Approach for Overcoming Knowledge Graph Sparsity | Automatic construction; Best effort; Centric expansion; Knowledge graphs; Real-world; Recent researches; Unstructured texts; Natural language processing systems(...) | Automatic construction of knowledge graphs (KGs) from unstructured text has received considerable attention in recent research, resulting in the construction of several KGs with millions of entities (nodes) and facts (edges) among them. Unfortunately, such KGs tend to be severely sparse in terms of number of facts known for a given entity, i.e., have low knowledge density. For example, the NELL KG consists of only 1.34 facts per entity. Unfortunately, such low knowledge density makes it challeng(...) | ACL | 2015 | 10.18653/v1/d15-1061 | Hegde M., Talukdar P. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84959894844&doi=10.18653%2fv1%2fd15-1061&partnerID=40&md5=293b887d578030d6f73fb6d89bcf3814 | India | entity extraction, relation extraction, entity classification | validation research | tool | - |
| Conference Paper | Answering Elementary Science Questions by Constructing Coherent Scenes Using Background Knowledge | Natural language processing systems; Back-ground knowledge; Competitive algorithms; Elementary science; Implicit informations; Knowledge graphs; Linguistic resources; Mental pictures; Multiple choice questions; Knowledge management(...) | Much of what we understand from text is not explicitly stated. Rather, the reader uses his/her knowledge to fill in gaps and create a coherent, mental picture or "scene" depicting what text appears to convey. The scene constitutes an understanding of the text, and can be used to answer questions that go beyond the text. Our goal is to answer elementary science questions, where this requirement is pervasive; A question will often give a partial description of a scene and ask the student about imp(...) | ACL | 2015 | 10.18653/v1/d15-1236 | Li Y., Clark P. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84959867303&doi=10.18653%2fv1%2fd15-1236&partnerID=40&md5=ca35d2968fd72770c7eabd9c013c4b46 | United States | question answering | validation research | tool | scholarly domain |
| Conference Paper | Enriching Word Embeddings Using Knowledge Graph for Semantic Tagging in Conversational Dialog Systems | Embeddings; Natural language processing systems; Semantics; Syntactics; Domain specific semantics; Knowledge graphs; Natural language queries; Objective functions; Semantic dependency; Semantic tagging; Syntactic dependencies; Word representations; Knowledge representation(...) | Unsupervised word embeddings provide rich linguistic and conceptual information about words. However, they may provide weak information about domain specific semantic relations for certain tasks such as semantic parsing of natural language queries, where such information about words can be valuable. To encode the prior know ledge about the semantic word relations, we present new method as follows: we extend the neural network based lexical word embedding objective function (Mikolov et al. 2013) (...) | Scopus | 2015 | - | Celikyilmaz A., Hakkani-Tiir D., Pasupat P., Sarikaya R. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84987629985&partnerID=40&md5=deb001215138bdab456b70f41603852e | United States | semantic parsing | validation research | technique | entertainment media |
| Conference Paper | Entity Translation with Collective Inference in Knowledge Graph | Collective learning; Knowledge base; Machine translation(...) | Nowadays knowledge base (KB) has been viewed as one of the important infrastructures for many web search applications and NLP tasks. However, in practice the availability of KB data varies from language to language, which greatly limits potential usage of knowledge base. In this paper, we propose a novel method to construct or enrich a knowledge base by entity translation with help of another KB but compiled in a different language. In our work, we concentrate on two key tasks: 1) collecting tra(...) | Scopus | 2015 | 10.1007/978-3-319-25207-0_5 | Li Q., Liu S., Lin R., Li M., Zhou M. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84951275676&doi=10.1007%2f978-3-319-25207-0_5&partnerID=40&md5=5981a3d872977177e02b88848fe0a77c | China | entity alignment, machine translation | validation research | method | entertainment media |
| Conference Paper | How to Build Templates for Rdf Question/Answering - an Uncertain Graph Similarity Join Approach | Computational linguistics; Benchmark datasets; Effectiveness and efficiencies; Knowledge graphs; Natural language questions; Natural languages; Pruning techniques; Template generation; Unstructured natural language; Natural language processing systems(...) | A challenging task in the natural language question answering (Q/A for short) over RDF knowledge graph is how to bridge the gap between unstructured natural language questions (NLQ) and graph-structured RDF data (G). One of the effective tools is the "template", which is often used in many existing RDF Q/A systems. However, few of them study how to generate templates automatically. To the best of our knowledge, we are the first to propose a join approach for template generation. Given a workload(...) | Scopus | 2015 | 10.1145/2723372.2747648 | Zheng W., Zou L., Lian X., Yu J.X., Song S., Zhao D. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84957575023&doi=10.1145%2f2723372.2747648&partnerID=40&md5=d6b0b8cd083030b7def968255e60b954 | China, Hong Kong, United States | question answering | validation research | method | - |
| Conference Paper | Learning Knowledge Graphs for Question Answering through Conversational Dialog | Computational linguistics; Domain model; General knowledge; Knowledge graphs; Natural languages; Query expansion; Question Answering; Question answering systems; Relation-based; Natural language processing systems(...) | We describe how a question-answering system can learn about its domain from conversational dialogs. Our system learns to relate concepts in science questions to propositions in a fact corpus, stores new concepts and relations in a knowledge graph (KG), and uses the graph to solve questions. We are the first to acquire knowledge for question-answering from open, natural language dialogs without a fixed ontology or domain model that predetermines what users can say. Our relation-based strategies c(...) | ACL | 2015 | 10.3115/v1/n15-1086 | Hixon B., Clark P., Hajishirzi H. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84959088414&doi=10.3115%2fv1%2fn15-1086&partnerID=40&md5=064a06b29ed8bf9c581f48ad5a1a98aa | United States | conversational interfaces, question answering | validation research | tool | scholarly domain |
| Conference Paper | Learning to Explain Entity Relationships in Knowledge Graphs | Computational linguistics; Baseline models; Entity-relationship; Human-readable; Knowledge graphs; Learning to rank; State of the art; Natural language processing systems(...) | We study the problem of explaining relationships between pairs of knowledge graph entities with human-readable descriptions. Our method extracts and enriches sentences that refer to an entity pair from a corpus and ranks the sentences according to how well they describe the relationship between the entities. We model this task as a learning to rank problem for sentences and employ a rich set of features. When evaluated on a large set of manually annotated sentences, we find that our method signi(...) | ACL | 2015 | 10.3115/v1/p15-1055 | Voskarides N., Meij E., Tsagkias M., De Rijke M., Weerkamp W. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84943740328&doi=10.3115%2fv1%2fp15-1055&partnerID=40&md5=8c07724f26c915d18f4f8a42a4218f51 | United Kingdom, Netherlands | semantic search | validation research | technique | - |
| Conference Paper | Matrix Factorization with Knowledge Graph Propagation for Unsupervised Spoken Language Understanding | Computational linguistics; Factorization; Matrix algebra; Ontology; Semantics; Speech processing; Corpus annotations; Domain specificity; Matrix factorizations; Pre-defined semantics; Propagation modeling; Semantic structures; Spoken dialogue system; Spoken language understanding; Natural language processing systems(...) | Spoken dialogue systems (SDS) typically require a predefined semantic ontology to train a spoken language understanding (SLU) module. In addition to the annotation cost, a key challenge for designing such an ontology is to define a coherent slot set while considering their complex relations. This paper introduces a novel matrix factorization (MF) approach to learn latent feature vectors for utterances and semantic elements without the need of corpus annotations. Specifically, our model learns th(...) | ACL | 2015 | 10.3115/v1/p15-1047 | Chen Y.-N., Wang W.Y., Gershman A., Rudnicky A.I. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84943809524&doi=10.3115%2fv1%2fp15-1047&partnerID=40&md5=ced451fd4d69f3a5fae48f44dd33ac73 | United States | semantic parsing | validation research | method | - |
| Conference Paper | Sanaphor: Ontology-Based Coreference Resolution | Arts computing; Knowledge representation; Natural language processing systems; Ontology; Co-reference resolutions; Inverted indices; Knowledge graphs; Semantic annotations; Semantic relatedness; Semantic-Web techniques; Splitting and merging; State-of-the-art techniques; Semantic Web(...) | We tackle the problem of resolving coreferences in textual content by leveraging Semantic Web techniques. Specifically, we focus on noun phrases that coreference identifiable entities that appear in the text; the challenge in this context is to improve the coreference resolution by leveraging potential semantic annotations that can be added to the identified mentions. Our system, SANAPHOR, first applies state-of-the-art techniques to extract entities, noun phrases, and candidate coreferences. Th(...) | Scopus | 2015 | 10.1007/978-3-319-25007-6_27 | Prokofyev R., Tonon A., Luggen M., Vouilloz L., Difallah D.E., Cudré-Mauroux P. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84952333039&doi=10.1007%2f978-3-319-25007-6_27&partnerID=40&md5=9dd9e49e2c2b9a33b1f09c507b62260c | Switzerland | text analysis | validation research | tool | - |
| Conference Paper | Semantics-Based Graph Approach to Complex Question-Answering | Computational linguistics; Natural language processing systems; Semantics; Architectural approach; Complex questions; Coreference; Cross validation; Knowledge graphs; Named entities; Proof of concept; Question Answering; Semantic roles; Syntactic dependencies; Knowledge graph(...) | This paper suggests an architectural approach of representing knowledge graph for complex question-answering. There are four kinds of entity relations added to our knowledge graph: syntactic dependencies, semantic role labels, named entities, and coreference links, which can be effectively applied to answer complex questions. As a proof of concept, we demonstrate how our knowledge graph can be used to solve complex questions such as arithmetics. Our experiment shows a promising result on solving(...) | ACL | 2015 | - | Jurczyk T., Choi J.D. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093129374&partnerID=40&md5=2400b8f1db2850418960e6b8dee57d5f | United States | question answering | solution proposal | technique | - |
| Conference Paper | Sematch: Semantic Entity Search from Knowledge Graph | Entity search; Knowledge graph; Query expansion; Semantic search; Semantic similarity(...) | As an increasing amount of the knowledge graph is published as Linked Open Data, semantic entity search is required to develop new applications. However, the use of structured query languages such as SPARQL is challenging for non-skilled users who need to master the query language as well as acquiring knowledge of the underlying ontology of Linked Data knowledge bases. In this article, we propose the Sematch framework for entity search in the knowledge graph that combines natural language query (...) | Scopus | 2015 | - | Zhu G., Iglesias C.A. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84964417432&partnerID=40&md5=272af308b0b35a7d840f7fd7aa67a194 | Spain | semantic search | validation research | tool | - |
| Conference Paper | A 2-Phase Frame-Based Knowledge Extraction Framework | Frame detection; Knowledge extraction; Natural language processing; Rdf knowledge graph; Sparql rules(...) | We present an approach for extracting knowledge from natural language English texts where processing is decoupled in two phases. The first phase comprises several standard NLP tasks whose results are integrated in a single RDF graph of mentions. The second phase processes the mention graph with SPARQL-like mapping rules to produce a knowledge graph organized around semantic frames (i.e., prototypical descriptions of events and situations). The decoupling allows: (i) choosing different tools for (...) | ACM | 2016 | 10.1145/2851613.2851845 | Corcoglioniti F., Rospocher M., Aprosio A.P. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84975887050&doi=10.1145%2f2851613.2851845&partnerID=40&md5=74d8dfb749aaf69ee62ee10c021762eb | Italy | entity extraction, relation extraction, entity linking | validation research | tool | - |
| Journal Article | Building Event-Centric Knowledge Graphs from News | Event-centric knowledge, Natural language processing, Event extraction, Information integration, Big data, Real world data(...) | Knowledge graphs have gained increasing popularity in the past couple of years, thanks to their adoption in everyday search engines. Typically, they consist of fairly static and encyclopedic facts about persons and organizations–e.g. a celebrity’s birth date, occupation and family members–obtained from large repositories such as Freebase or Wikipedia. In this paper, we present a method and tools to automatically build knowledge graphs from news articles. As news articles describe changes in the (...) | ScienceDirect | 2016 | 10.1016/j.websem.2015.12.004 | Marco Rospocher and Marieke {van Erp} and Piek Vossen and Antske Fokkens and Itziar Aldabe and German Rigau and Aitor Soroa and Thomas Ploeger and Tessel Bogaard | https://www.sciencedirect.com/science/article/pii/S1570826815001456 | Italy, Netherlands, Spain | entity extraction, relation extraction, entity linking | solution proposal | tool | news |
| Conference Paper | Constraint-Based Question Answering with Knowledge Graph | Computational linguistics; Benchmark data; Constraint-based; Knowledge base; Knowledge based; Knowledge graphs; Multi-constraints; Question Answering; State-of-the-art methods; Knowledge based systems(...) | WebQuestions and SimpleQuestions are two benchmark data-sets commonly used in recent knowledge-based question answering (KBQA) work. Most questions in them are 'simple' questions which can be answered based on a single relation in the knowledge base. Such data-sets lack the capability of evaluating KBQA systems on complicated questions. Motivated by this issue, we release a new data-set, namely ComplexQuestions1 aiming to measure the quality of KBQA systems on 'multi-constraint' questions which (...) | ACL | 2016 | - | Bao J., Duan N., Yan Z., Zhou M., Zhao T. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85034261067&partnerID=40&md5=869fde72520a8d3d7450bf206dfd5fd2 | China | question answering | validation research | technique; resource | - |
| Conference Paper | Constructing Curriculum Ontology and Dynamic Learning Path Based on Resource Description Framework | Curriculum; Education; Knowledge graph; Learning path; Linked data; Natural language processing; Ontology; Resource description framework(...) | Curriculum for school is generated based on the academic year. Be-cause students have to study several subjects each and every year, the relative topics are put into curricula in discrete. In this study, we propose a method to construct a dynamic learning path which enables us to learn the relative topics continuously. In this process, we define two kinds of similarity score, inher-itance score and context similarity score to connect the learning path of relative topics. We also construct curric(...) | Scopus | 2016 | - | Urakawa M., Miyazaki M., Fujisawa H., Naemura M., Yamada I. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84992431083&partnerID=40&md5=2c828c4d4074e157a5bdbdcc256874a2 | Japan | semantic search | solution proposal | tool | education |
| Conference Paper | Digitalhistorian: Search & Analytics Using Annotations | Natural language processing systems; Query processing; Semantics; Digital Documents; Digital humanities; Document collection; Knowledge graphs; Retrieval systems; Semantic annotations; State-of-the-art methods; Temporal expressions; Information retrieval(...) | Born-digital document collections contain vast amounts of historical facts and knowledge. However, manual assessment of these large text collections is infeasible. In this paper, we demonstrate a retrieval system, DIGITALHISTORIAN, that analyzes these document collections using semantic annotations in the form of temporal expressions and named entities linked to a knowledge graph. For queries about entities or events DIGITALHISTORIAN utilizes state-of-the-art methods to understand and analyze te(...) | Scopus | 2016 | - | Gupta D., Strötgen J., Berberich K. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84983549712&partnerID=40&md5=112e2ab2179c04eb72e69d05d0bc724f | Germany | semantic search | solution proposal | tool | history |
| Journal Article | Frame-Based Ontology Population with Pikes | FrameBase; natural language processing; Ontology population; semantic role labeling; Semantic Web(...) | We present an approach for ontology population from natural language English texts that extracts RDF triples according to FrameBase, a Semantic Web ontology derived from FrameNet. Processing is decoupled in two independently-tunable phases. First, text is processed by several NLP tasks, including Semantic Role Labeling (SRL), whose results are integrated in an RDF graph of mentions, i.e., snippets of text denoting some entity/fact. Then, the mention graph is processed with SPARQL-like rules usin(...) | IEEE | 2016 | 10.1109/tkde.2016.2602206 | Corcoglioniti F., Rospocher M., Aprosio A.P. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84996478366&doi=10.1109%2fTKDE.2016.2602206&partnerID=40&md5=7c4327818df442d9c25eb0795b3e669d | Italy | entity extraction, relation extraction, ontology construction | validation research | tool | - |
| Conference Paper | Framester: a Wide Coverage Linguistic Linked Data Hub | Frame detection; Frame semantics; FrameNet; Framenet coverage; Framester; Knowledge graphs; Linguistic linked data(...) | Semantic web applications leveraging NLP can benefit from easy access to expressive lexical resources such as FrameNet. However, the usefulness of FrameNet is affected by its limited coverage and nonstandard semantics. The access to existing linguistic resources is also limited because of poor connectivity among them. We present some strategies based on Linguistic Linked Data to broaden FrameNet coverage and formal linkage of lexical and factual resources. We created a novel resource, Framester,(...) | Scopus | 2016 | 10.1007/978-3-319-49004-5_16 | Gangemi A., Alam M., Asprino L., Presutti V., Recupero D.R. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84997124448&doi=10.1007%2f978-3-319-49004-5_16&partnerID=40&md5=cdc7212b9de89606b0d5a31cdceae74c | France, Italy | ontology construction, entity alignment | validation research | resource | - |
| Conference Paper | Knowledge-Driven Event Embedding for Stock Prediction | Commerce; Computational linguistics; Financial markets; Forecasting; Natural language processing systems; Semantics; Vector spaces; Accurate prediction; Back-ground knowledge; Continuous spaces; Event representations; Objective functions; Semantic information; Stock market prediction; Stock market volatility; Knowledge management(...) | Representing structured events as vectors in continuous space offers a new way for defining dense features for natural language processing (NLP) applications. Prior work has proposed effective methods to learn event representations that can capture syntactic and semantic information over text corpus, demonstrating their effectiveness for downstream tasks such as event-driven stock prediction. On the other hand, events extracted from raw texts do not contain background knowledge on entities and r(...) | ACL | 2016 | - | Ding X., Zhang Y., Liu T., Duan J. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85051146159&partnerID=40&md5=b392aa1f2bf3a29ea48ad7df6175ff10 | China, Singapore | knowledge graph embedding | validation research | technique | business |
| Journal Article | Learning Better Word Embedding by Asymmetric Low-Rank Projection of Knowledge Graph | knowledge graph; natural language processing; neural network; word embedding(...) | Word embedding, which refers to low-dimensional dense vector representations of natural words, has demon-strated its power in many natural language processing tasks. However, it may suffer from the inaccurate and incomplete information contained in the free text corpus as training data. To tackle this challenge, there have been quite a few studies that leverage knowledge graphs as an additional information source to improve the quality of word embedding. Although these studies have achieved cert(...) | Scopus | 2016 | 10.1007/s11390-016-1651-5 | Tian F., Gao B., Chen E.-H., Liu T.-Y. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84969920163&doi=10.1007%2fs11390-016-1651-5&partnerID=40&md5=0ca88e505f792fd1149ac1e3712af344 | China | knowledge graph embedding, text analysis, natural language inference | validation research | technique | - |
| Conference Paper | Relation Schema Induction Using Tensor Factorization with Side Information | Automation; Factorization; Tensors; Automatic identification; Knowledge graphs; Medical research; Real-world datasets; Side information; State of the art; Tensor factorization; Natural language processing systems(...) | Given a set of documents from a specific domain (e.g., medical research journals), how do we automatically build a Knowledge Graph (KG) for that domain? Automatic identification of relations and their schemas, i.e., type signature of arguments of relations (e.g., undergo(Patient, Surgery)), is an important first step towards this goal. We refer to this problem as Relation Schema Induction (RSI). In this paper, we propose Schema Induction using Coupled Tensor Factorization (SICTF), a novel tensor(...) | ACL | 2016 | 10.18653/v1/d16-1040 | Nimishakavi M., Saini U.S., Talukdar P. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072831168&doi=10.18653%2fv1%2fd16-1040&partnerID=40&md5=278dcee15ccc743912d48979442e98e4 | India | ontology construction | validation research | method | - |
| Journal Article | Research on Ontology Non-Taxonomic Relations Extraction in Plant Domain Knowledge Graph Construction | Baidu Encyclopedia; Knowledge graph; Non-taxonomic relation; Ontology learning; Plant domain ontology(...) | In order to provide more specific knowledge and technology of plant field, the main task of KG (knowledge graph) is to extract a wealth of concepts and relationships. Due to the relation extraction is the most difficult in KG construction, this paper makes use of ontology learning, and proposes a non-taxonomic relation learning method to obtain representative concepts and their relations from unstructured and semi-structured texts of Baidu Encyclopedia entry content by using lexicon-syntactic pa(...) | Scopus | 2016 | 10.6041/j.issn.1000-1298.2016.09.038 | Zhao M., Du Y., Du H., Zhang J., Wang H., Chen Y. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84988660563&doi=10.6041%2fj.issn.1000-1298.2016.09.038&partnerID=40&md5=f578bbb0a9afd1a77fbfa35c2e6698ee | China | entity extraction, relation extraction, ontology construction | validation research | method | agriculture |
| Conference Paper | The Role of the Wordnet Relations in the Knowledge-Basedword Sense Disambiguation Task | Knowledge based systems; Natural language processing systems; Semantics; Knowledge based; Knowledge graphs; Semantic relations; Test sets; Word Sense Disambiguation; Wordnet; Ontology(...) | In this paper we present an analysis of different semantic relations extracted from WordNet, Extended WordNet and Sem-Cor, with respect to their role in the task of knowledge-based word sense disambiguation. The experiments use the same algorithm and the same test sets, but different variants of the knowledge graph. The results show that different sets of relations have different impact on the results: positive or negative. The beneficial ones are discussed with respect to the combination of rel(...) | Scopus | 2016 | - | Simov K., Popov A., Osenova P. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84962860263&partnerID=40&md5=3ab548c97fe84345aa1605171213d5dc | Bulgaria | text analysis | validation research | guidelines | - |
| Conference Paper | The Semantic Knowledge Graph: a Compact, Auto-Generated Model for Real-Time Traversal and Ranking of Any Relationship Within a Domain | Anomaly Detection; Graph Compression; Information Retrieval; Knowledge Graph; Natural Language Processing; Ontology Learning; Relationship Extraction; Semantic Search; Text Analytics(...) | This paper describes a new kind of knowledge representation and mining system which we are calling the Semantic Knowledge Graph. At its heart, the Semantic Knowledge Graph leverages an inverted index, along with a complementary uninverted index, to represent nodes (terms) and edges (the documents within intersecting postings lists for multiple terms/nodes). This provides a layer of indirection between each pair of nodes and their corresponding edge, enabling edges to materialize dynamically from(...) | IEEE | 2016 | 10.1109/dsaa.2016.51 | Grainger T., Aljadda K., Korayem M., Smith A. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85011264001&doi=10.1109%2fDSAA.2016.51&partnerID=40&md5=fd4a3c8640ff6b4594e55e9455ac9bdc | United States | entity extraction, relation extraction, ontology construction, semantic search | validation research | tool | business |
| Journal Article | A Knowledge Graph Based Speech Interface for Question Answering Systems | Spoken question answering, Knowledge graphs, Automatic speech recognition, Spoken language understanding, Spoken interface, Linked data(...) | Speech interfaces to conversational systems have been a focus in academia and industry for over a decade due to its applicability as a natural interface. Speech recognition and speech synthesis constitute the important input and output modules respectively for such spoken interface systems. In this paper, the speech recognition interface for question answering applications is reviewed, and existing limitations are discussed. The existing spoken question answering (QA) systems use an automatic sp(...) | ScienceDirect | 2017 | 10.1016/j.specom.2017.05.001 | Ashwini {Jaya Kumar} and Christoph Schmidt and Joachim Köhler | https://www.sciencedirect.com/science/article/pii/S0167639316301443 | Germany | question answering | solution proposal | method | - |
| Conference Paper | An Investigative Search Engine for the Human Trafficking Domain | Human trafficking; Illicit domains; Investigative search; Knowledge graph construction; Knowledge graphs(...) | Enabling intelligent search systems that can navigate and facet on entities, classes and relationships, rather than plain text, to answer questions in complex domains is a longstanding aspect of the Semantic Web vision. This paper presents an investigative search engine that meets some of these challenges, at scale, for a variety of complex queries in the human trafficking domain. The engine provides a real-world case study of synergy between technology derived from research communities as diver(...) | Scopus | 2017 | 10.1007/978-3-319-68204-4_25 | Kejriwal M., Szekely P. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032171447&doi=10.1007%2f978-3-319-68204-4_25&partnerID=40&md5=ae88cc0e6341b348cecd40f6bb20fb58 | United States | entity extraction, relation extraction, semantic search | evaluation research | tool | law |
| Conference Paper | Armatweet: Detecting Events by Semantic Tweet Analysis | Disasters; Search engines; Social networking (online); Knowledge graphs; Natural disasters; Science and Technology; Semantic event detection; Social media analysis; Sparql queries; Swiss Armed Forces; Terrorist activities; Semantic Web(...) | Armasuisse Science and Technology, the R&D agency for the Swiss Armed Forces, is developing a Social Media Analysis (SMA) system to help detect events such as natural disasters and terrorist activity by analysing Twitter posts. The system currently supports only keyword search, which cannot identify complex events such as ‘politician dying’ or ‘militia terror act’ since the keywords that correctly identify such events are typically unknown. In this paper we present ArmaTweet, an extension of SMA(...) | Scopus | 2017 | 10.1007/978-3-319-58451-5_10 | Tonon A., Cudré-Mauroux P., Blarer A., Lenders V., Motik B. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019637337&doi=10.1007%2f978-3-319-58451-5_10&partnerID=40&md5=dd623c35f634a194a7029fa06f42bf96 | Switzerland, United Kingdom | entity extraction, relation extraction, semantic search | validation research | tool | public sector; social media |
| Conference Paper | Capturing Knowledge in Semantically-Typed Relational Patterns to Enhance Relation Linking | Knowledge Capture; Knowledge Graphs; Question Answering Systems; Relation Linking(...) | Transforming natural language questions into formal queries is an integral task in Question Answering (QA) systems. QA systems built on knowledge graphs like DBpedia, require a step after natural language processing for linking words, specifically including named entities and relations, to their corresponding entities in a knowledge graph. To achieve this task, several approaches rely on background knowledge bases containing semantically-typed relations, e.g., PATTY, for an extra disambiguation (...) | ACM | 2017 | 10.1145/3148011.3148031 | Singh K., Mulang I.O., Lytra I., Jaradeh M.Y., Sakor A., Vidal M.-E., Lange C., Auer S. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85040631730&doi=10.1145%2f3148011.3148031&partnerID=40&md5=02b470de9f8d8ee03358cef89641997f | Germany | relation linking | validation research | technique | - |
| Conference Paper | Conceptnet 55: an Open Multilingual Graph of General Knowledge | - | Machine learning about language can be improved by supplying it with specific knowledge and sources of external information. We present here a new version of the linked open data resource ConceptNet that is particularly well suited to be used with modern NLP techniques such as word embeddings. ConceptNet is a knowledge graph that connects words and phrases of natural language with labeled edges. Its knowledge is collected from many sources that include expert created resources, crowd-sourcing, a(...) | WoS | 2017 | - | Speer R,Chin J,Havasi C | https://arxiv.org/pdf/1612.03975.pdf | United States | semantic search | validation research | resource | - |
| Conference Paper | Cross-Modal Knowledge Transfer: Improving the Word Embedding of Apple by Looking at Oranges | Knowledge Transfer; Multi-Modality; Word Similarity(...) | Capturing knowledge via learned latent vector representations of words, images and knowledge graph (KG) entities has shown state of-the-art performance in computer vision, computational linguistics and KG tasks. Recent results demonstrate that the learning of such representations across modalities can be beneficial, since each modality captures complementary information. However, those approaches are limited to concepts with cross-modal alignments in the training data which are only available fo(...) | ACM | 2017 | 10.1145/3148011.3148026 | Both F., Thoma S., Rettinger A. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85040602084&doi=10.1145%2f3148011.3148026&partnerID=40&md5=824cabcd87f1f2c599a636242b7ee82f | Germany | augmented language models | validation research | technique | - |
| Journal Article | Cross-Sentence N-Ary Relation Extraction with Graph Lstms | - | Past work in relation extraction has focused on binary relations in single sentences. Recent NLP inroads in high-value domains have sparked interest in the more general setting of extracting n-ary relations that span multiple sentences. In this paper, we explore a general relation extraction framework based on graph long short-term memory networks (graph LSTMs) that can be easily extended to cross-sentence n-ary relation extraction. The graph formulation provides a unified way of exploring diffe(...) | ACL | 2017 | 10.1162/tacl_a_00049 | Peng, Nanyun and Poon, Hoifung and Quirk, Chris and Toutanova, Kristina and Yih, Wen-tau | https://aclanthology.org/Q17-1008 | United States | relation extraction, augmented language models | validation research | technique | health |
| Conference Paper | Generating Natural Language Question-Answer Pairs from a Knowledge Graph Using a Rnn Based Question Generation Model | Computational linguistics; Knowledge representation; Automatically generated; Downstream applications; Factoid questions; Natural language questions; Question-answer pairs; Sequence modeling; State of the art; Template based methods; Natural language processing systems(...) | In recent years, knowledge graphs such as Freebase that capture facts about entities and relationships between them have been used actively for answering factoid questions. In this paper, we explore the problem of automatically generating question answer pairs from a given knowledge graph. The generated question answer (QA) pairs can be used in several downstream applications. For example, they could be used for training better QA systems. To generate such QA pairs, we first extract a set of key(...) | ACL | 2017 | 10.18653/v1/e17-1036 | Indurthi S., Raghu D., Khapra M.M., Joshi S. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021669255&doi=10.18653%2fv1%2fe17-1036&partnerID=40&md5=a0c5d4c9b14990e04e1684a304c25941 | India | question answering, question generation | validation research | technique | - |
| Journal Article | Intelligent Learning for Knowledge Graph Towards Geological Data | Geology; Natural language processing systems; Ontology; Semantics; Application systems; Document pre-processing; Effectiveness and efficiencies; Geological information; Intelligent learning; Knowledge extraction; NAtural language processing; Semantic associations; Data mining(...) | Knowledge graph (KG) as a popular semantic network has been widely used. It provides an effective way to describe semantic entities and their relationships by extending ontology in the entity level. This article focuses on the application of KG in the traditional geological field and proposes a novel method to construct KG. On the basis of natural language processing (NLP) and data mining (DM) algorithms, we analyze those key technologies for designing a KG towards geological data, including geo(...) | Scopus | 2017 | 10.1155/2017/5072427 | Zhu Y., Zhou W., Xu Y., Liu J., Tan Y. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85014189855&doi=10.1155%2f2017%2f5072427&partnerID=40&md5=29b4ca77c0fb930ce97a117cddbf2ccb | China | entity extraction, relation extraction, ontology construction | solution proposal | method | natural science |
| Conference Paper | Knowledge Graph: Semantic Representation and Assessment of Innovation Ecosystems | Competence analysis; Competence assessment; Competence detection; Computational linguistics; Corporate strategy; Data mining; Decision making; Expert matching; Expert mining; Information extraction; Information retrieval; Innovation ecosystem; Knowledge graph; Knowledge representation; Machine learning; Name disambiguation; Name normalization; Natural language processing; Ontology; Patent analysis; Question-answering; Reasoning; Semantic analysis; Semantic technologies(...) | Innovative capacity is highly dependent upon knowledge and the possession of unique competences can be an important source of enduring strategic advantage. Hence, being able to identify, locate, measure, and assess competence occupants can be a decisive competitive edge. In this work, we introduce a framework that assists with performing such tasks. To achieve this, NLP-, rule-based, and machine learning techniques are employed to process raw data such as academic publications or patents. The fr(...) | Scopus | 2017 | 10.1007/978-3-319-69548-8_15 | Ulmschneider K., Glimm B. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85034227560&doi=10.1007%2f978-3-319-69548-8_15&partnerID=40&md5=e3e7ac8e9d38de71c02d73b255d1fd7b | Germany | entity extraction, relation extraction, semantic search | solution proposal | method | business |
| Conference Paper | Knowledge Qestions from Knowledge Graphs | Information retrieval; Natural language processing systems; Document collection; Historical data; Knowledge graphs; Logistic regression classifier; Multiple choice questions; Natural language questions; Structured queries; Template based methods; Query processing(...) | We address the problem of automatically generating quiz-style knowledge questions from a knowledge graph such as DBpedia. Qestions of this kind have ample applications, for instance, to educate users about or to evaluate their knowledge in a specific domain. To solve the problem, we propose a novel end-to-end approach. The approach first selects a named entity from the knowledge graph as an answer. It then generates a structured triple-pattern query, which yields the answer as its sole result. I(...) | Scopus | 2017 | 10.1145/3121050.3121073 | Seyler D., Yahya M., Berberich K. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85033238128&doi=10.1145%2f3121050.3121073&partnerID=40&md5=aa1629dd0304b8502db08c1f7b38984d | Germany, United Kingdom, United States | question generation, question answering | validation research | technique | - |
| Conference Paper | Learning Multi-Faceted Knowledge Graph Embeddings for Natural Language Processing | Artificial intelligence; Embeddings; Learning algorithms; Knowledge graphs; NAtural language processing; Related works; Relational properties; Wide spectrum; Natural language processing systems(...) | Knowledge graphs have challenged the existing embedding-based approaches for representing their multifacetedness. To address some of the issues, we have investigated some novel approaches that (i) capture the multilingual transitions on different language-specific versions of knowledge, and (ii) encode the commonly existing monolingual knowledge with important relational properties and hierarchies. In addition, we propose the use of our approaches in a wide spectrum of NLP tasks that have not be(...) | Scopus | 2017 | 10.24963/ijcai.2017/744 | Chen M., Zaniolo C. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85031917956&doi=10.24963%2fijcai.2017%2f744&partnerID=40&md5=cd70b8e5cef2d02a4147a63a3b55d373 | United States | knowledge graph embedding | solution proposal | technique | - |
| Conference Paper | Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings | Linguistics; Semantics; Speech processing; Dialogue systems; Human dialogues; Human evaluation; Human like; Knowledge graphs; Neural modeling; Rule-based models; Structured knowledge; Computational linguistics(...) | We study a symmetric collaborative dialogue setting in which two agents, each with private knowledge, must strategically communicate to achieve a common goal. The open-ended dialogue state in this setting poses new challenges for existing dialogue systems. We collected a dataset of 11K human-human dialogues, which exhibits interesting lexical, semantic, and strategic elements. To model both structured knowledge and unstructured language, we propose a neural model with dynamic knowledge graph emb(...) | ACL | 2017 | 10.18653/v1/p17-1162 | He H., Balakrishnan A., Eric M., Liang P. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85040931116&doi=10.18653%2fv1%2fP17-1162&partnerID=40&md5=c6484eddfd9dbadea164ad122779386a | United States | conversational interfaces, knowledge graph embedding | validation research | tool; resource | - |
| Conference Paper | Marine Variable Linker: Exploring Relations between Changing Variables in Marine Science Literature | Computational linguistics; Demonstrations; Graphical user interfaces; Knowledge representation; Text mining; Causal relations; Co-occurrence; Interactive way; Knowledge graphs; Marine science; Marine scientists; Web based; Carbon dioxide(...) | We report on a demonstration system for text mining of literature in marine science and related disciplines. It automatically extracts variables (e.g. CO2) involved in events of change/increase/decrease (e.g increasing CO2), as well as cooccurrence and causal relations among these events (e.g. increasing CO2 causes a decrease in pH in seawater), resulting in a big knowledge graph. A web-based graphical user interface targeted at marine scientists facilitates searching, browsing and visualising e(...) | ACL | 2017 | 10.18653/v1/e17-3023 | Marsi E., Øzturk P., Ardelan M.V. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021681301&doi=10.18653%2fv1%2fe17-3023&partnerID=40&md5=3df635031763e232a2f01d33ce24dc06 | Norway | entity extraction, relation extraction, semantic search | solution proposal | tool | natural science |
| Conference Paper | Programming Bots by Synthesizing Natural Language Expressions into Api Invocations | Botnet; Knowledge management; Learning systems; Software engineering; Complex applications; Development community; Entity recognition; Knowledge graphs; Lines of code; Natural language expressions; Natural languages; Real-world; Application programming interfaces (API)(...) | At present, bots are still in their preliminary stages of development. Many are relatively simple, or developed ad-hoc for a very specific use-case. For this reason, they are typically programmed manually, or utilize machine-learning classifiers to interpret a fixed set of user utterances. In reality, real world conversations with humans require support for dynamically capturing users expressions. Moreover, bots will derive immeasurable value by programming them to invoke APIs for their results.(...) | IEEE | 2017 | 10.1109/ase.2017.8115694 | Zamanirad S., Benatallah B., Barukh M.C., Casati F., Rodriguez C. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041448452&doi=10.1109%2fASE.2017.8115694&partnerID=40&md5=56e2ba36ccd6dc295937710f0751a7f4 | Australia, Italy, Russian Federation | conversational interfaces | solution proposal | method | engineering |
| Conference Paper | Recognizing Mentions of Adverse Drug Reaction in Social Media Using Knowledge-Infused Recurrent Models | Computational linguistics; Recurrent neural networks; Social networking (online); Adverse drug reactions; Annotation tool; Context dependent; Expert annotations; Highly accurate; Knowledge graphs; Recurrent models; Recurrent neural network (RNN); Pharmacodynamics(...) | Recognizing mentions of Adverse Drug Reactions (ADR) in social media is challenging: ADR mentions are contextdependent and include long, varied and unconventional descriptions as compared to more formal medical symptom terminology. We use the CADEC corpus to train a recurrent neural network (RNN) transducer, integrated with knowledge graph embeddings of DBpedia, and show the resulting model to be highly accurate (93.4 F1). Furthermore, even when lacking high quality expert annotations, we show t(...) | ACL | 2017 | 10.18653/v1/e17-1014 | Stanovsky G., Gruhl D., Mendes P.N. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021649640&doi=10.18653%2fv1%2fe17-1014&partnerID=40&md5=be3fb20651f6379666265ea716297679 | Israel, United States | augmented language models, text classification | validation research | technique | social media; health |
| Conference Paper | Sparsity and Noise: Where Knowledge Graph Embeddings Fall Short | Errors; Natural language processing systems; Benchmark datasets; Embedding technique; Empirical experiments; Knowledge graphs; Low-dimensional representation; Relationships between entities; Embeddings(...) | Knowledge graph (KG) embedding techniques use structured relationships between entities to learn low-dimensional representations of entities and relations. One prominent goal of these approaches is to improve the quality of knowledge graphs by removing errors and adding missing facts. Surprisingly, most embedding techniques have been evaluated on benchmark datasets consisting of dense and reliable subsets of human-curated KGs, which tend to be fairly complete and have few errors. In this paper, (...) | ACL | 2017 | 10.18653/v1/d17-1184 | Pujara J., Augustine E., Getoor L. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85055864552&doi=10.18653%2fv1%2fd17-1184&partnerID=40&md5=8e874f5aa0770ba757d26eabcb18ed5a | United States | knowledge graph embedding | validation research | guidelines | - |
| Conference Paper | Srdf: a Novel Lexical Knowledge Graph for Whole Sentence Knowledge Extraction | Lexical knowledge graph; Natural language processing; Open information extraction; Question answering; Semantic web(...) | In this paper, we present a novel lexical knowledge graph called SRDF and describe an extraction system that automatically generates a SRDF graph from the Korean natural language sentence. In the semantic web, knowledge is expressed in the RDF triple form but natural language sentences consist of multiple relationships between the predicates and arguments. For this reason, we design a SRDF graph structure that combines open information extraction method with reification for the whole sentence kn(...) | Scopus | 2017 | 10.1007/978-3-319-59888-8_27 | Nam S., Choi G., Choi K.-S. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021190357&doi=10.1007%2f978-3-319-59888-8_27&partnerID=40&md5=b162408a3c1e1bb9776d38379aa78248 | South Korea | entity extraction, relation extraction, entity linking | solution proposal | resource; tool | - |
| Conference Paper | The Arabic Knowledge Graph: Opportunities and Challenges | Arabic Knowledge Graph; Challenges; Linked Data; Opportunities; Semantic Web(...) | Semantic Web has brought forth the idea of computing with knowledge, hence, attributing the ability of thinking to machines. Knowledge Graphs represent a major advancement in the construction of the Web of Data where machines are context-aware when answering users' queries. The English Knowledge Graph was a milestone realized by Google in 2012. Even though it is a useful source of information for English users and applications, it does not offer much for the Arabic users and applications. In thi(...) | IEEE | 2017 | 10.1109/icsc.2017.22 | Ktob A., Li Z. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018265429&doi=10.1109%2fICSC.2017.22&partnerID=40&md5=b8b2e9a682a79659a37287eafb138c56 | China | entity extraction, relation extraction, ontology construction | solution proposal | method; guidelines | - |
| Conference Paper | Towards Lexical Chains for Knowledge-Graph-Based Word Embeddings | Chains; Deep learning; Graphic methods; Linguistics; Data sparseness; Embeddings; Knowledge graphs; Lexical Chain; Linguistic values; Natural language text; Wikipedia; Word vectors; Natural language processing systems(...) | Word vectors with varying dimensionalities and produced by different algorithms have been extensively used in NLP. The corpora that the algorithms are trained on can contain either natural language text (e.g. Wikipedia or newswire articles) or artificially-generated pseudo corpora due to natural data sparseness. We exploit Lexical Chain based templates over Knowledge Graph for generating pseudo-corpora with controlled linguistic value. These corpora are then used for learning word embeddings. A (...) | ACL | 2017 | 10.26615/978-954-452-049-6-087 | Simov K., Boytcheva S., Osenova P. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045766451&doi=10.26615%2f978-954-452-049-6-087&partnerID=40&md5=b184f7213d9a24aeb09144fb1a85109e | Bulgaria | augmented language models, text generation | validation research | technique | - |
| Conference Paper | Triple Prediction from Texts by Using Distributed Representations of Words | Distributed representations of words; Knowledge extraction; Knowledge graph completion(...) | Knowledge graphs have been shown to be useful to many tasks in artificial intelligence. Triples of knowledge graphs are traditionally structured by human editors or extracted from semi-structured information; however, editing is expensive, and semi-structured information is not common. On the other hand, most such information is stored as text. Hence, it is necessary to develop a method that can extract knowledge from texts and then construct or populate a knowledge graph; this has been attempte(...) | Scopus | 2017 | 10.1587/transinf.2017edp7112 | Ebisu T., Ichise R. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85038370232&doi=10.1587%2ftransinf.2017EDP7112&partnerID=40&md5=abde520637a26b4a98203e0abf597760 | Japan | entity extraction, relation extraction | validation research | technique | - |
| Conference Paper | A Neural Question Answering Model Based on Semi-Structured Tables | Computational linguistics; End-to-end systems; Knowledge graphs; Model-based OPC; Multiple-choice questions; Question Answering; Question answering systems; Semi-structured; State of the art; Structured knowledge; Text corpora; Knowledge graph(...) | Most question answering (QA) systems are based on raw text and structured knowledge graph. However, raw text corpora are hard for QA system to understand, and structured knowledge graph needs intensive manual work, while it is relatively easy to obtain semi-structured tables from many sources directly, or build them automatically. In this paper, we build an end-to-end system to answer multiple choice questions with semi-structured tables as its knowledge. Our system answers queries by two steps.(...) | ACL | 2018 | - | Wang H., Zhang X., Ma S., Sun X., Wang H., Wang M. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108798869&partnerID=40&md5=749ef04ce6beca0769a96fbd7fac66fd | China | question answering | validation research | method | - |
| Conference Paper | Accurate Text-Enhanced Knowledge Graph Representation Learning | Classification (of information); Computational linguistics; Knowledge representation; Semantics; Text processing; Attention mechanisms; Classification tasks; Knowledge graphs; Learning methods; Learning techniques; State-of-the-art performance; Textual information; Textual representation; Learning systems(...) | Previous representation learning techniques for knowledge graph representation usually represent the same entity or relation in different triples with the same representation, without considering the ambiguity of relations and entities. To appropriately handle the semantic variety of entities/relations in distinct triples, we propose an accurate text-enhanced knowledge graph representation learning method, which can represent a relation/entity with different representations in different triples (...) | ACL | 2018 | - | An B., Chen B., Han X., Sun L. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065643962&partnerID=40&md5=cf0de02b4f0b09bf79a22ebb07737b95 | China | knowledge graph embedding | validation research | technique | - |
| Conference Paper | Automatic Assessment of Conceptual Text Complexity Using Knowledge Graphs | Computational linguistics; Graphic methods; Text processing; Automatic assessment; Binary classification; Classification tasks; Discriminative power; Graph-based; High quality; Knowledge graphs; Large knowledge basis; Learner corpora; Simple++; Knowledge graph(...) | Complexity of texts is usually assessed only at the lexical and syntactic levels. Although it is known that conceptual complexity plays a significant role in text understanding, no attempts have been made at assessing it automatically. We propose to automatically estimate the conceptual complexity of texts by exploiting a number of graph-based measures on a large knowledge base. By using a high-quality language learners corpus for English, we show that graph-based measures of individual text con(...) | ACL | 2018 | - | Štajner S., Hulpuş I. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113796054&partnerID=40&md5=41dfe972929ede701199ee6b7358cd91 | Germany | text classification | validation research | technique | - |
| Conference Paper | Big Open-Source Social Science: Capabilities and Methodology for Automating Social Science Analytics | automated social science; multi-modal data fusion; social network analysis; Social situational awareness(...) | Currently, obtaining reliable situational awareness of the social landscape is an arduous, lengthy process involving manual analyses by social scientists. These traditional methods do not scale to the speed and diversity required by DoD operations or the high-speed, international business model in today's corporate environment. Conversely, "big data" easily scales to meet these challenges but lacks the rigor of social science theory. We present Big Open-Source Social Science (BOSSS), a research (...) | Scopus | 2018 | 10.1117/12.2306500 | Palladino A., Bienenstock E.J., George C.A., Moore K.E. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85049673329&doi=10.1117%2f12.2306500&partnerID=40&md5=d256fa38de4f87c28170e06232b570d6 | United States | entity extraction, relation extraction, semantic search | solution proposal | method | social science |
| Conference Paper | Boosting Text Classification Performance on Sexist Tweets by Text Augmentation and Text Generation Using a Combination of Knowledge Graphs | - | Text classification models have been heavily utilized for a slew of interesting natural language processing problems. Like any other machine learning model, these classifiers are very dependent on the size and quality of the training dataset. Insufficient and imbalanced datasets will lead to poor performance. An interesting solution to poor datasets is to take advantage of the world knowledge in the form of knowledge graphs to improve our training data. In this paper, we use ConceptNet and Wikid(...) | ACL | 2018 | 10.18653/v1/w18-5114 | Sharifirad, Sima and Jafarpour, Borna and Matwin, Stan | https://aclanthology.org/W18-5114 | Canada | text classification, text generation | validation research | method | social media |
| Conference Paper | Cl Scholar: the Acl Anthology Knowledge Graph Miner | - | We present CL Scholar, the ACL Anthology knowledge graph miner to facilitate high-quality search and exploration of current research progress in the computational linguistics community. In contrast to previous works, periodically crawling, indexing and processing of new incoming articles is completely automated in the current system. CL Scholar utilizes both textual and network information for knowledge graph construction. As an additional novel initiative, CL Scholar supports more than 1200 sch(...) | ACL | 2018 | 10.18653/v1/n18-5004 | Singh, Mayank and Dogga, Pradeep and Patro, Sohan and Barnwal, Dhiraj and Dutt, Ritam and Haldar, Rajarshi and Goyal, Pawan and Mukherjee, Animesh | https://aclanthology.org/N18-5004 | India | entity extraction, relation extraction, semantic search | solution proposal | tool | scholarly domain |
| Conference Paper | Co-Training Embeddings of Knowledge Graphs and Entity Descriptions for Cross-Lingual Entity Alignment | Artificial intelligence; Large dataset; Semantics; Co-training; Cross-lingual; Knowledge graphs; Latent semantics; Semi-supervised; Structured knowledge; Wikipedia; Embeddings(...) | Multilingual knowledge graph (KG) embeddings provide latent semantic representations of entities and structured knowledge with cross-lingual inferences, which benefit various knowledge-driven cross-lingual NLP tasks. However, precisely learning such cross-lingual inferences is usually hindered by the low coverage of entity alignment in many KGs. Since many multilingual KGs also provide literal descriptions of entities, in this paper, we introduce an embedding-based approach which leverages a wea(...) | Scopus | 2018 | 10.24963/ijcai.2018/556 | Chen M., Tian Y., Chang K.-W., Skiena S., Zaniolo C. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85055705769&doi=10.24963%2fijcai.2018%2f556&partnerID=40&md5=6842c775fa6e625c8cc0d86867b2dd74 | United States | entity alignment, knowledge graph embedding | validation research | technique | - |
| Conference Paper | Commonsense Knowledge Aware Conversation Generation with Graph Attention | Artificial intelligence; Encoding (symbols); Knowledge based systems; Natural language processing systems; Semantics; Attention mechanisms; Commonsense knowledge; Knowledge base; Knowledge graphs; Language understanding; NAtural language processing; Semantic information; State of the art; Graphic methods(...) | Commonsense knowledge is vital to many natural language processing tasks. In this paper, we present a novel open-domain conversation generation model to demonstrate how large-scale commonsense knowledge can facilitate language understanding and generation. Given a user post, the model retrieves relevant knowledge graphs from a knowledge base and then encodes the graphs with a static graph attention mechanism, which augments the semantic information of the post and thus supports better understand(...) | Scopus | 2018 | 10.24963/ijcai.2018/643 | Zhou H., Young T., Huang M., Zhao H., Xu J., Zhu X. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85055701005&doi=10.24963%2fijcai.2018%2f643&partnerID=40&md5=30fb7d91d04da226c2891179047f5e29 | China | text generation, conversational interfaces | validation research | method | - |
| Conference Paper | Complex Sequential Question Answering: Towards Learning to Converse over Linked Question Answer Pairs with a Knowledge Graph | Artificial intelligence; Natural language processing systems; Knowledge graphs; Natural language questions; Question Answering; Question-answer pairs; Real world setting; Real-world scenario; Semi-automatics; State of the art; Query processing(...) | While conversing with chatbots, humans typically tend to ask many questions, a significant portion of which can be answered by referring to large-scale knowledge graphs (KG). While Question Answering (QA) and dialog systems have been studied independently, there is a need to study them closely to evaluate such real-world scenarios faced by bots involving both these tasks. Towards this end, we introduce the task of Complex Sequential QA which combines the two tasks of (i) answering factual questi(...) | Scopus | 2018 | - | Saha A., Pahuja V., Khapra M.M., Sankaranarayanan K., Chandar S. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060473640&partnerID=40&md5=ef0c0573598eb4d1f0183fe2858540d6 | Canada, India, United States | question answering, conversational interfaces | validation research | technique; resource | - |
| Conference Paper | Cooperative Denoising for Distantly Supervised Relation Extraction | Computational linguistics; Distillation; Extraction; Knowledge management; Bi-directional; De-noising; Knowledge graphs; Labelings; Mutual learning; Performance; Relation extraction; State-of-the-art methods; Text corpora; Unstructured texts; Knowledge graph(...) | Distantly supervised relation extraction greatly reduces human efforts in extracting relational facts from unstructured texts. However, it suffers from noisy labeling problem, which can degrade its performance. Meanwhile, the useful information expressed in knowledge graph is still underutilized in the state-of-the-art methods for distantly supervised relation extraction. In the light of these challenges, we propose CORD, a novel COopeRative Denoising framework, which consists two base networks (...) | ACL | 2018 | - | Lei K., Chen D., Li Y., Du N., Yang M., Fan W., Shen Y. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119405619&partnerID=40&md5=2d338ee255c369b1941010d748a8e7ab | China | relation extraction | validation research | technique | - |
| Conference Paper | Dbtravel: a Tourism-Oriented Semantic Graph | DBpedia; Name entity recognition; Wikitravel(...) | We present DBtravel, a tourism-oriented knowledge graph generated from the collaborative travel site Wikitravel. Our approach takes advantage of the recommended guideline for contributors provided by Wikitravel and extracts the named entities available in Wikitravel Spanish entries by using a NLP pipeline. Compared to a manually annotated gold standard, results show that our approach reaches values for precision and recall around 80% for some sections of Wikitravel for the Spanish language. © Sp(...) | Scopus | 2018 | 10.1007/978-3-030-03056-8_19 | Calleja P., Priyatna F., Mihindukulasooriya N., Rico M. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85058284497&doi=10.1007%2f978-3-030-03056-8_19&partnerID=40&md5=42a71f4bfde83c4de026726dfcf11402 | Spain | entity extraction, relation extraction, ontology construction | solution proposal | tool; resource | tourism |
| Conference Paper | Elden: Improved Entity Linking Using Densified Knowledge Graphs | Arts computing; Embeddings; Benchmark datasets; Co-occurrence statistics; Degree of connectivity; Entity similarities; Knowledge graphs; State of the art; Text corpora; Computational linguistics(...) | Entity Linking (EL) systems aim to automatically map mentions of an entity in text to the corresponding entity in a Knowledge Graph (KG). Degree of connectivity of an entity in the KG directly affects an EL system's ability to correctly link mentions in text to the entity in KG. This causes many EL systems to perform well for entities well connected to other entities in KG, bringing into focus the role of KG density in EL. In this paper, we propose Entity Linking using Densified Knowledge Graphs(...) | ACL | 2018 | - | Radhakrishnan P., Talukdar P., Varma V. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083504420&partnerID=40&md5=dbb1b33c24df23e2de72e92771f5e6cf | India | entity linking | validation research | tool | - |
| Conference Paper | Enriching Word Embeddings with Domain Knowledge for Readability Assessment | Computational linguistics; Domain Knowledge; Knowledge graph; Semantics; Domain knowledge; Embeddings; Knowledge graphs; Learn+; Loss functions; Semantic relations; Word level; Embeddings(...) | In this paper, we present a method which learns the word embedding for readability assessment. For the existing word embedding models, they typically focus on the syntactic or semantic relations of words, while ignoring the reading difficulty, thus they may not be suitable for readability assessment. Hence, we provide the knowledge-enriched word embedding (KEWE), which encodes the knowledge on reading difficulty into the representation of words. Specifically, we extract the knowledge on word-lev(...) | ACL | 2018 | - | Jiang Z., Gu Q., Yin Y., Chen D. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119407962&partnerID=40&md5=5e5d5bf1d59a8e84a572d8b7dd592b78 | China | text classification | validation research | technique | - |
| Conference Paper | Entity-Duet Neural Ranking: Understanding the Role of Knowledge Graph Semantics in Neural Information Retrieval | Computational linguistics; Information retrieval; Distributed representation; End to end; Generalization ability; Knowledge graphs; Ranking model; Search system; Two-component; Semantics(...) | This paper presents the Entity-Duet Neural Ranking Model (EDRM), which introduces knowledge graphs to neural search systems. EDRM represents queries and documents by their words and entity annotations. The semantics from knowledge graphs are integrated in the distributed representations of their entities, while the ranking is conducted by interaction-based neural ranking networks. The two components are learned end-to-end, making EDRM a natural combination of entity-oriented search and neural in(...) | ACL | 2018 | 10.18653/v1/p18-1223 | Liu Z., Xiong C., Sun M., Liu Z. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061193161&doi=10.18653%2fv1%2fp18-1223&partnerID=40&md5=7fd9ea97f0d33a051b0d7f2ed46380cc | China, United States | semantic search | validation research | technique | - |
| Conference Paper | Farewell Freebase: Migrating the Simplequestions Dataset to Dbpedia | Computational linguistics; Mapping; Natural language processing systems; Benchmark datasets; Dbpedia; Knowledge graphs; Lookups; Natural language questions; Non-trivial; Question Answering; Question answering systems; Real-world; Simple++; Knowledge graph(...) | Question answering over knowledge graphs is an important problem of interest both commercially and academically. There is substantial interest in the class of natural language questions that can be answered via the lookup of a single fact, driven by the availability of the popular SIMPLEQUESTIONS dataset. The problem with this dataset, however, is that answer triples are provided from Freebase, which has been defunct for several years. As a result, it is difficult to build “real-world” question (...) | ACL | 2018 | - | Azmy M., Shi P., Lin J., Ilyas I.F. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85059516001&partnerID=40&md5=a0320632393ad98021bf13988601f5ff | Canada | question answering, entity alignment | validation research | resource | - |
| Conference Paper | Generating Fine-Grained Open Vocabulary Entity Type Descriptions | Dynamic contexts; Dynamic memory; Entity-types; Fine grained; Knowledge graphs; Textual description; Computational linguistics(...) | While large-scale knowledge graphs provide vast amounts of structured facts about entities, a short textual description can often be useful to succinctly characterize an entity and its type. Unfortunately, many knowledge graph entities lack such textual descriptions. In this paper, we introduce a dynamic memory-based network that generates a short open vocabulary description of an entity by jointly leveraging induced fact embeddings as well as the dynamic context of the generated sequence of wor(...) | ACL | 2018 | 10.18653/v1/p18-1081 | Bhowmik R., De Melo G. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063099681&doi=10.18653%2fv1%2fp18-1081&partnerID=40&md5=15fca4c5111b0b729e1f41e051c865a6 | United States | data-to-text generation, augmented language models | validation research | technique | - |
| Conference Paper | Gkr: the Graphical Knowledge Representation for Semantic Parsing | - | This paper describes the first version of an open-source semantic parser that creates graphical representations of sentences to be used for further semantic processing, e.g. for natural language inference, reasoning and semantic similarity. The Graphical Knowledge Representation which is output by the parser is inspired by the Abstract Knowledge Representation, which separates out conceptual and contextual levels of representation that deal respectively with the subject matter of a sentence and (...) | ACL | 2018 | 10.18653/v1/w18-1304 | Kalouli, Aikaterini-Lida and Crouch, Richard | https://aclanthology.org/W18-1304 | Germany, United States | semantic parsing | validation research | tool | - |
| Conference Paper | Graph Embedding Based Query Construction over Knowledge Graphs | Knowledge graph; Knowledge graph embedding; Natural language question answering; Query construction(...) | Graph-structured queries provide an efficient way to retrieve the desired data from large-scale knowledge graphs. However, it is difficult for non-expert users to write such queries, and users prefer expressing their query intention through natural language questions. Therefore, automatically constructing graph-structured queries of given natural language questions has received wide attention in recent years. Most existing methods rely on natural language processing techniques to perform the que(...) | IEEE | 2018 | 10.1109/icbk.2018.00009 | Wang R., Wang M., Liu J., Yao S., Zheng Q. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061368151&doi=10.1109%2fICBK.2018.00009&partnerID=40&md5=49caf2aa9e0874c02c32174f9252d0ae | China | question answering, knowledge graph embedding | validation research | method | - |
| Conference Paper | Gre: an Adaptive and Personalized Exercise Model for K-12 Online Education | Ebbinghaus Forgetting Curve; K-12 Online Education; Knowledge Graph; Personalized Exercise; Speech Recognition(...) | In this paper, we propose an adaptive and personalized exercise model for K-12 online education. It consists of knowledge Graph, knowledge components(KCs) Recognition and Exercises generation. The model builds up knowledge graph of students by processing and analyzing their exercise behaviors, recognizes knowledge components from audio recordings of online tutoring automated by utilizing speech recognition and natural language processing, and generates a list of exercises based on Ebbinghaus for(...) | ACM | 2018 | 10.1145/3291078.3291118 | Gong T.-J., Yao X.-X., Ma W. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061285555&doi=10.1145%2f3291078.3291118&partnerID=40&md5=62f5b9a544dd473bac2684d493c96271 | China | entity extraction, semantic search | evaluation research | tool | education |
| Conference Paper | Improving Api Caveats Accessibility by Mining Api Caveats Knowledge Graph | API caveats; Coreference Resolution; Entity Linking; Knowledge Graph(...) | API documentation provides important knowledge about the functionality and usage of APIs. In this paper, we focus on API caveats that developers should be aware of in order to avoid unintended use of an API. Our formative study of Stack Overflow questions suggests that API caveats are often scattered in multiple API documents, and are buried in lengthy textual descriptions. These characteristics make the API caveats less discoverable. When developers fail to notice API caveats, it is very likely(...) | IEEE | 2018 | 10.1109/icsme.2018.00028 | Li H., Li S., Sun J., Xing Z., Peng X., Liu M., Zhao X. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85058287370&doi=10.1109%2fICSME.2018.00028&partnerID=40&md5=4ef27c53df73fcaa669364c3d75f6673 | Australia, China, Singapore | entity extraction, relation extraction, entity linking, semantic search | validation research | method | engineering |
| Journal Article | Information Extraction and Knowledge Graph Construction from Geoscience Literature | Chinese word segmentation; Chord and bigram graphs; Geological corpus; Geoscience literature; Knowledge graph(...) | Geoscience literature published online is an important part of open data, and brings both challenges and opportunities for data analysis. Compared with studies of numerical geoscience data, there are limited works on information extraction and knowledge discovery from textual geoscience data. This paper presents a workflow and a few empirical case studies for that topic, with a focus on documents written in Chinese. First, we set up a hybrid corpus combining the generic and geology terms from ge(...) | ScienceDirect | 2018 | 10.1016/j.cageo.2017.12.007 | Wang C., Ma X., Chen J., Chen J. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85039732196&doi=10.1016%2fj.cageo.2017.12.007&partnerID=40&md5=036ffb8dd33000dbb8efc6fd3ba9afa3 | China, United States | entity extraction, relation extraction | solution proposal | method | natural science |
| Conference Paper | Joint Entity and Relation Linking Using Earl | Entity Linking; Question Answering; Relation Linking(...) | In order to answer natural language questions over knowledge graphs, most processing pipelines involve entity and relation linking. Traditionally, entity linking and relation linking have been performed either as dependent sequential tasks or independent parallel tasks. In this demo paper, we present EARL, which performs entity linking and relation linking as a joint single task. The system determines the best semantic connection between all keywords of the question by referring to the knowledge(...) | Scopus | 2018 | - | Banerjee D., Dubey M., Chaudhuri D., Lehmann J. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85055354595&partnerID=40&md5=41e9eb05d3899dd42ebb7be107c9fff4 | Germany | entity linking, relation linking, question answering | validation research | tool | - |
| Conference Paper | Jointly Embedding Entities and Text with Distant Supervision | - | Learning representations for knowledge base entities and concepts is becoming increasingly important for NLP applications. However, recent entity embedding methods have relied on structured resources that are expensive to create for new domains and corpora. We present a distantly-supervised method for jointly learning embeddings of entities and text from an unnanotated corpus, using only a list of mappings between entities and surface forms. We learn embeddings from open-domain and biomedical co(...) | ACL | 2018 | - | Newman-Griffis D,Lai AM,Fosler-Lussier E | https://aclanthology.org/W18-3026.pdf | United States | knowledge graph embedding | validation research | technique | - |
| Conference Paper | Knadia: Enterprise Knowledge Assisted Dialogue Systems Using Deep Learning | AI chatbots; chatbot; conversational agents; Conversational Dialogue System; Conversational Systems; Deep Learning; digital persona; Intent Identification; knowledge graph; knowledge synthesis; natural language processing; virtual assistance(...) | In this paper we present the design, architecture and implementation of KNADIA, a conversational dialogue system for intra-enterprise use, providing knowledge-Assisted question answering and transactional assistance to employees of a large organization. KNADIA has been deployed in production in TCS, a large organization with over 380,000 employees distributed globally; the system is currently supporting a few thousand active users making hundreds of queries per day. We identify, define and disti(...) | IEEE | 2018 | 10.1109/icde.2018.00161 | Singh M., Agarwal P., Chaudhary A., Shroff G., Khurana P., Patidar M., Bisht V., Bansal R., Sachan P., Kumar R. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057107255&doi=10.1109%2fICDE.2018.00161&partnerID=40&md5=de77865f9876ed63936b94bb540cd284 | India | conversational interfaces, question answering | evaluation research | tool; guidelines | business |
| Journal Article | Knowledge Graph Based on Domain Ontology and Natural Language Processing Technology for Chinese Intangible Cultural Heritage | Deep learning; Domain ontology; Intangible cultural heritage; Knowledge graph; Natural language processing; The 24 solar terms(...) | Intangible cultural heritage (ICH) is a precious historical and cultural resource of a country. Protection and inheritance of ICH is important to the sustainable development of national culture. There are many different intangible cultural heritage items in China. With the development of information technology, ICH database resources were built by government departments or public cultural services institutions, but most databases were widely dispersed. Certain traditional database systems are di(...) | ScienceDirect | 2018 | 10.1016/j.jvlc.2018.06.005 | Dou J., Qin J., Jin Z., Li Z. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050818908&doi=10.1016%2fj.jvlc.2018.06.005&partnerID=40&md5=55f351fc9593735ccd67714e8291520f | China | entity extraction, relation extraction, ontology construction | solution proposal | method | culture |
| Conference Paper | Knowledge-Enriched Two-Layered Attention Network for Sentiment Analysis | Computational linguistics; Sentiment analysis; Support vector regression; Benchmark datasets; External knowledge; Knowledge graphs; Model-based OPC; Multi-layer perceptron networks; Network-based; State-of-the-art system; Word net; Network layers(...) | We propose a novel two-layered attention network based on Bidirectional Long Short-Term Memory for sentiment analysis. The novel two-layered attention network takes advantage of the external knowledge bases to improve the sentiment prediction. It uses the Knowledge Graph Embedding generated using the Word- Net. We build our model by combining the two-layered attention network with the supervised model based on Support Vector Regression using a Multilayer Perceptron network for sentiment analysis(...) | ACL | 2018 | - | Kumar A., Kawahara D., Kurohashi S. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85059660544&partnerID=40&md5=4ec5e2b4078d1f79d129334ef87dfc49 | India, Japan | augmented language models, text analysis | validation research | technique | - |
| Conference Paper | Learning Beyond Datasets: Knowledge Graph Augmented Neural Networks for Natural Language Processing | Computational linguistics; Deep learning; Knowledge based systems; Labeled data; Learning algorithms; Natural language processing systems; Text processing; Attention mechanisms; Enhance learning; Knowledge graphs; Labeled training data; NAtural language processing; Natural languages; Prior information; Text classification; Learning systems(...) | Machine Learning has been the quintessential solution for many AI problems, but learning models are heavily dependent on specific training data. Some learning models can be incorporated with prior knowledge using a Bayesian setup, but these learning models do not have the ability to access any organized world knowledge on demand. In this work, we propose to enhance learning models with world knowledge in the form of Knowledge Graph (KG) fact triples for Natural Language Processing (NLP) tasks. O(...) | ACL | 2018 | - | Annervaz K.M., Chowdhury S.B.R., Dukkipati A. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075751697&partnerID=40&md5=53881b9376f21b7c2dd77a987ac41ebb | India | augmented language models | validation research | technique | - |
| Journal Article | Multiview Clustering Via Unified and View-Specific Embeddings Learning | Incomplete multiview data; knowledge graph embedding; multiview learning; subspace learning(...) | Multiview clustering, which aims at using multiple distinct feature sets to boost clustering performance, has a wide range of applications. A subspace-based approach, a type of widely used methods, learns unified embedding from multiple sources of information and gives a relatively good performance. However, these methods usually ignore data similarity rankings; for example, example A may be more similar to B than C, and such similarity triplets may be more effective in revealing the data cluste(...) | IEEE | 2018 | 10.1109/tnnls.2017.2786743 | Yin Q., Wu S., Wang L. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85043366384&doi=10.1109%2fTNNLS.2017.2786743&partnerID=40&md5=fa92ff5c6dbae29b0f71c2746298fef9 | China | knowledge graph embedding | validation research | technique | - |
| Journal Article | Natural Language Processing for Music Knowledge Discovery | entity linking; information extraction; Musicology; natural language processing; sentiment analysis(...) | Today, a massive amount of musical knowledge is stored in written form, with testimonies dated as far back as several centuries ago. In this work, we present different Natural Language Processing (NLP) approaches to harness the potential of these text collections for automatic music knowledge discovery, covering different phases in a prototypical NLP pipeline, namely corpus compilation, text-mining, information extraction, knowledge graph generation, and sentiment analysis. Each of these approac(...) | Scopus | 2018 | 10.1080/09298215.2018.1488878 | Oramas S., Espinosa-Anke L., Gómez F., Serra X. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85049580619&doi=10.1080%2f09298215.2018.1488878&partnerID=40&md5=2a5e0cc960847581c532f2aefd706b35 | Spain, United Kingdom | entity extraction, relation extraction, entity linking, semantic search | solution proposal | method; guidelines | entertainment media |
| Conference Paper | Open-World Knowledge Graph Completion | Neural networks; Closed world assumption; Convolutional neural network; Embeddings; Filling in; Knowledge graphs; Large datasets; Link prediction; Web searches; Natural language processing systems(...) | Knowledge Graphs (KGs) have been applied to many tasks including Web search, link prediction, recommendation, natural language processing, and entity linking. However, most KGs are far from complete and are growing at a rapid pace. To address these problems, Knowledge Graph Completion (KGC) has been proposed to improve KGs by filling in its missing connections. Unlike existing methods which hold a closed-world assumption, i.e., where KGs are fixed and new entities cannot be easily added, in the (...) | Scopus | 2018 | - | Shi B., Weninger T. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85056498188&partnerID=40&md5=63fcef4c8de3a789f08f50abbb4eca9e | United States | knowledge graph embedding, entity classification, link prediction | validation research | tool | - |
| Conference Paper | Relation Linking for Wikidata Using Bag of Distribution Representation | Knowledge graph; NLP; Relation linking(...) | Knowledge graphs (KGs) are essential repositories of structured and semi-structured knowledge which benefit various NLP applications. To utilize the knowledge in KGs to help machines to better understand plain texts, one needs to bridge the gap between knowledge and texts. In this paper, a Relation Linking System for Wikidata (RLSW) is proposed to link the relations in KGs to plain texts. The proposed system uses the knowledge in Wikidata as seeds and clusters relation mentions in text with a no(...) | Scopus | 2018 | 10.1007/978-3-319-73618-1_55 | Yang X., Ren S., Li Y., Shen K., Li Z., Wang G. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041237683&doi=10.1007%2f978-3-319-73618-1_55&partnerID=40&md5=ae21a3a73a0a85eab250dd087fa09bc7 | China | relation linking | validation research | method | - |
| Conference Paper | Research Progress of Knowledge Graph Based on Knowledge Base Embedding | Deep learning; Knowledge embedding; Knowledge graph; Knowledge representation(...) | The knowledge Graph (KGs) is a valuable tool and useful resource to describe the entities and their relationships in various natural language processing tasks. Especially, the insufficient semantic of entities and relationship in text limited the efficiency and accuracy of knowledge representation. With the increasing of knowledge base resources, many scholars began to study the knowledge graph’s construction technology based on knowledge base embedding. The basic idea is that the knowledge grap(...) | Scopus | 2018 | 10.1007/978-981-13-2206-8_16 | Caifang T., Yuan R., Hualei Y., Jiamin C. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053994602&doi=10.1007%2f978-981-13-2206-8_16&partnerID=40&md5=1c589bd6a56e3e0d4e907314836d064f | China | knowledge graph embedding | secondary research | guidelines | - |
| Conference Paper | Retrofitting Distributional Embeddings to Knowledge Graphs with Functional Relations | Computational linguistics; Embeddings; Encoding (symbols); Graphic methods; Natural language processing systems; Retrofitting; Semantics; 'current; Data relationships; Embeddings; Extract informations; Functional relation; Knowledge graphs; Learn+; Penalty function; Structured data; Unstructured data; Knowledge graph(...) | Knowledge graphs are a versatile framework to encode richly structured data relationships, but it can be challenging to combine these graphs with unstructured data. Methods for retrofitting pre-trained entity representations to the structure of a knowledge graph typically assume that entities are embedded in a connected space and that relations imply similarity. However, useful knowledge graphs often contain diverse entities and relations (with potentially disjoint underlying corpora) which do n(...) | ACL | 2018 | - | Lengerich B.J., Maas A.L., Potts C. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083094891&partnerID=40&md5=1b543e2a44f15bc08c2a72105b4779ea | United States | knowledge graph embedding | validation research | tool | - |
| Conference Paper | Sentence Comprehension and Semantic Syntheses by Cognitive Machine Learning | AI; Algorithms; Cognitive computing; Cognitive systems; Computational intelligence; Computational linguistics; Machine knowledge learning; Natural language processing; Semantic computing(...) | Recent development in machine learning and computational linguistics has enabled cognitive machines to understand the semantics of human expressions. A system for sentence syntactic analysis and semantic synthesis is developed based on denotational mathematics. Machine sentence learning and comprehension are reduced to the building of a composed concept that maps the semantics of the subject onto the counterpart of object(s) represented by formal concepts and phrases. A set of semantic operation(...) | IEEE | 2018 | 10.1109/icci-cc.2018.8482024 | Valipour M., Wang Y. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85056452607&doi=10.1109%2fICCI-CC.2018.8482024&partnerID=40&md5=27da2970d1bc95a2a2b80b882e9140ef | Canada | semantic parsing | solution proposal | technique | - |
| Conference Paper | T-Know: a Knowledge Graph-Based Question Answering and Infor-Mation Retrieval System for Traditional Chinese Medicine | - | T-Know is a knowledge service system based on the constructed knowledge graph of Traditional Chinese Medicine (TCM). Using authorized and anonymized clinical records, medicine clinical guidelines, teaching materials, classic medical books, academic publications, etc., as data resources, the system extracts triples from free texts to build a TCM knowledge graph by our developed natural language processing methods. On the basis of the knowledge graph, a deep learning algorithm is implemented for s(...) | ACL | 2018 | - | Liu, Ziqing and Peng, Enwei and Yan, Shixing and Li, Guozheng and Hao, Tianyong | https://aclanthology.org/C18-2004 | China | question answering, semantic search | solution proposal | tool | health |
| Conference Paper | The Whyis Knowledge Graph Framework in Action | Computer software reusability; Learning algorithms; Learning systems; Pipelines; Semantics; Complex applications; Deductive reasoning; Health informatics; Knowledge curation; Multiple data sources; Predictive models; Research and development; Semantic-analytics; Natural language processing systems(...) | We will demonstrate a reusable framework for developing knowledge graphs that supports general, open-ended development of knowledge curation, interaction, and inference. Knowledge graphs need to be easily maintainable and usable in sometimes complex application settings. Often, scaling knowledge graph updates can require developing a knowledge curation pipeline that either replaces the graph wholesale whenever updates are made, or requires detailed tracking of knowledge provenance across multipl(...) | Scopus | 2018 | - | McCusker J.P., Rashid S.M., Agu N., Bennett K.P., McGuinness D.L. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85055314766&partnerID=40&md5=78a6009ee8ea00dc13b4330c896629e2 | United States | semantic search | solution proposal | tool | - |
| Conference Paper | Towards Building a Knowledge Graph with Open Data - a Roadmap | Knowledge graph; Open data(...) | With the increasing interest in knowledge graph over the years, several approaches have been proposed for building knowledge graphs. Most of the recent approaches involve using semi-structured sources such as Wikipedia or information crawled from the web using a combination of extraction methods and Natural Language Processing (NLP) techniques. In most cases, these approaches tend to make a compromise between accuracy and completeness. In our ongoing work, we examine a technique for building a k(...) | Scopus | 2018 | 10.1007/978-3-319-98827-6_13 | Musa Aliyu F., Ojo A. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85052830661&doi=10.1007%2f978-3-319-98827-6_13&partnerID=40&md5=9f314ffdeeab53f27006d8c39358af5c | Ireland, Niger, Nigeria | entity extraction, relation extraction | solution proposal | method | - |
| Conference Paper | Unsupervised Abstractive Meeting Summarization with Multi-Sentence Compression and Budgeted Submodular Maximization | - | We introduce a novel graph-based framework for abstractive meeting speech summarization that is fully unsupervised and does not rely on any annotations. Our work combines the strengths of multiple recent approaches while addressing their weaknesses. Moreover, we leverage recent advances in word embeddings and graph degeneracy applied to NLP to take exterior semantic knowledge into account, and to design custom diversity and informativeness measures. Experiments on the AMI and ICSI corpus show th(...) | ACL | 2018 | 10.18653/v1/p18-1062 | Shang, Guokan and Ding, Wensi and Zhang, Zekun and Tixier, Antoine and Meladianos, Polykarpos and Vazirgiannis, Michalis and Lorr{'e}, Jean-Pierre | https://aclanthology.org/P18-1062 | France, Greece | augmented language models, text summarization | validation research | tool | - |
| Journal Article | Using Multiple Web Resources and Inference Rules to Classify Chinese Word Semantic Relation | Chinese word semantic relation; Inference rules; Lexical relation; Morpho syntactics; Ontology; Semantic relation classification(...) | Purpose: The purpose of this paper is to classify Chinese word semantic relations, which are synonyms, antonyms, hyponyms and meronymys. Design/methodology/approach: Basically, four simple methods are applied, ontology-based, dictionary-based, pattern-based and morpho-syntactic method. The authors make good use of search engine to build lexical and semantic resources for dictionary-based and pattern-based methods. To improve classification performance with more external resources, they also clas(...) | Scopus | 2018 | 10.1108/idd-03-2018-0010 | Ma S., Zhang Y., Zhang C. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85051177141&doi=10.1108%2fIDD-03-2018-0010&partnerID=40&md5=26f5fc665c2e4d820442ba81a146c6ee | China | relation classification | solution proposal | method | - |
| Conference Paper | Variational Reasoning for Question Answering with Knowledge Graph | Artificial intelligence; Benchmarking; Learning algorithms; Natural language processing systems; Benchmark datasets; Knowledge graphs; Learning architectures; Logic reasoning; Question Answering; Question-answer pairs; State-of-the-art performance; Translation models; Deep learning(...) | Knowledge graph (KG) is known to be helpful for the task of question answering (QA), since it provides well-structured relational information between entities, and allows one to further infer indirect facts. However, it is challenging to build QA systems which can learn to reason over knowledge graphs based on question-answer pairs alone. First, when people ask questions, their expressions are noisy (for example, typos in texts, or variations in pronunciations), which is non-trivial for the QA s(...) | Scopus | 2018 | - | Zhang Y., Dai H., Kozareva Z., Smola A.J., Song L. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060464851&partnerID=40&md5=e20549798b3f7ebbb7a6ede335984a21 | Georgia, United States | question answering | validation research | technique | - |
| Conference Paper | Voice of the Customer Oriented New Product Synthesis over Knowledge Graphs | Electronic commerce; Manufacture; Natural language processing systems; Sales; Communication gaps; Hierarchical product; Ontological models; Product manufacturers; Product synthesis; Reasoning techniques; Voice of customer; Voice of the customer; Product design(...) | The online shopping has been much easier and popular, and meanwhile brings new challenges and opportunities to the field of product design and marketing sale. On one hand, product manufacturers find it challenging to produce new popularly accepted products to meet the customers’ needs; on the other hand, end customers usually feel it difficult to buy ideal goods that they really want, even if navigating a huge amount of commodities. There are indeed a’communication gap’ between the customers and(...) | Scopus | 2018 | 10.1115/detc201885909 | Qin F., Xu H., Zhang W., Yuan L., Li M., Liu Y., Liu Y., Chen Y. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85056885168&doi=10.1115%2fDETC201885909&partnerID=40&md5=4609180e34b615dc17144397aa4a787f | China, United Kingdom | semantic search | solution proposal | method | business |
| Conference Paper | Zeroshot Multimodal Named Entity Disambiguation for Noisy Social Media Posts | Computational linguistics; Knowledge based systems; External knowledge; Image caption; Knowledge graphs; Multimodal network; Named entities; Named entity disambiguations; State of the art; Training sets; Social networking (online)(...) | We introduce the new Multimodal Named Entity Disambiguation (MNED) task for multimodal social media posts such as Snapchat or Instagram captions, which are composed of short captions with accompanying images. Social media posts bring significant challenges for disambiguation tasks because 1) ambiguity not only comes from polysemous entities, but also from inconsistent or incomplete notations, 2) very limited context is provided with surrounding words, and 3) there are many emerging entities ofte(...) | ACL | 2018 | - | Moon S., Neves L., Carvalho V. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063072770&partnerID=40&md5=62fa27e5949feb05727ce374c5b53d84 | United States | entity linking | validation research | technique; resource | social media |
| Journal Article | A Comprehensive Framework for Ontology Based Classifier Using Unstructured Data | Feature Hashing; Knowledge Graphs; Multiclass classification; Ontology; Text categorization; Topic Modeling(...) | The knowledge contained within the natural language data can be used to build expert systems. Classifying unstructured data using ontology and text classification algorithms to extract information is one way of approaching the problem of building intelligent systems. One major problem with text processing is most data generated is unstructured and ambiguous, as, data with a structure helps to identify meaningful patterns and eventually exhibit the latent knowledge. Ambiguity in natural language (...) | Scopus | 2019 | 10.35940/ijeat.a2042.109119 | Thangaraj M., Sivakami M. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075291008&doi=10.35940%2fijeat.A2042.109119&partnerID=40&md5=063a175982918a56be7bfdfb2793dc1d | India | text classification | validation research | method | - |
| Journal Article | A Deep Neural Network Model for Joint Entity and Relation Extraction | Automatic knowledge graph construction; deep neural networks; entity and relation extraction; natural language processing; pointer networks; relational triplet extraction; sequence-to-sequence learning(...) | Joint extraction of entities and their relations from the text is an essential issue in automatic knowledge graph construction, which is also known as the joint extraction of relational triplets. The relational triplets in sentence are complicated, multiple and different relational triplets may have overlaps, which is commonly seen in reality. However, multiple pairs of triplets cannot be efficiently extracted in most of the previous works. To mitigate this problem, we propose a deep neural netw(...) | IEEE | 2019 | 10.1109/access.2019.2949086 | Pang Y., Liu J., Liu L., Yu Z., Zhang K. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077241235&doi=10.1109%2fACCESS.2019.2949086&partnerID=40&md5=f44050826fcd5d272602eb42755f91af | China | entity extraction, relation extraction | validation research | technique | - |
| Conference Paper | A General Process for the Semantic Annotation and Enrichment of Electronic Program Guides | Electronic programming guides; Natural language processing; Semantic enrichment; Word embeddings(...) | Electronic Program Guides (EPGs) are usual resources aimed to inform the audience about the programming being transmitted by TV stations and cable/satellite TV providers. However, they only provide basic metadata about the TV programs, while users may want to obtain additional information related to the content they are currently watching. This paper proposes a general process for the semantic annotation and subsequent enrichment of EPGs using external knowledge bases and natural language proces(...) | Scopus | 2019 | 10.1007/978-3-030-21395-4_6 | Gonzalez-Toral S., Espinoza-Mejia M., Palacio-Baus K., Saquicela V. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85066117874&doi=10.1007%2f978-3-030-21395-4_6&partnerID=40&md5=eb0c586e64af57ec86124b4996db0299 | Ecuador | semantic search | validation research | method | entertainment media |
| Conference Paper | A Knowledge Graph Based Approach for Automatic Speech and Essay Summarization | Knowledge Graphs; Named Entity Recognition; NLP; Speech Analysis(...) | Every day, big amounts of unstructured data is generated. This data is of the form of essays, research papers, speeches, patents, scholastic articles, book chapters etc. In today's world, it is very important to extract key patterns from huge text passages or verbal speeches. This paper proposes a novel method for summarizing multilingual vocal as well as written paragraphs and speeches, using semantic Knowledge Graphs. Using the proposed model, big text extracts or speeches can be summarized fo(...) | Scopus | 2019 | 10.1109/i2ct45611.2019.9033908 | Khadilkar K., Kulkarni S., Venkatraman S. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083072791&doi=10.1109%2fI2CT45611.2019.9033908&partnerID=40&md5=52dd58fcd8695857771e14b3307126c1 | Australia, India | entity extraction, relation extraction | solution proposal | method | - |
| Conference Paper | A Methodology for Extracting Knowledge about Controlled Vocabularies from Textual Data Using Fca-Based Ontology Engineering | Controlled vocabulary; Formal Concept Analysis; Natural Language Processing; Ontology learning; Semantic knowledge extraction(...) | We introduce an end-to-end methodology (from text processing to querying a knowledge graph) for the sake of knowledge extraction from text corpora with a focus on a list of vocabularies of interest. We propose a pipeline that incorporates Natural Language Processing (NLP), Formal Concept Analysis (FCA), and Ontology Engineering techniques to build an ontology from textual data. We then extract the knowledge about controlled vocabularies by querying that knowledge graph, i.e., the engineered onto(...) | IEEE | 2019 | 10.1109/bibm.2018.8621239 | Jabbari S., Stoffel K. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062533489&doi=10.1109%2fBIBM.2018.8621239&partnerID=40&md5=51d5ab6af8286e2415b936bae2f93c5e | Switzerland | ontology construction, semantic search | solution proposal | method | - |
| Conference Paper | A Semantic Approach for Automating Knowledge in Policies of Cyber Insurance Services | Cyber Insurance; Knowledge Representation; Ontology; Policies(...) | With the rapid adoption of web services, the need to protect against various threats has become imperative for organizations operating in cyberspace. Organizations are increasingly opting to get financial cover in the event of losses due to a security incident. This helps them safeguard against the threat posed to third-party services that the organization uses. It is in the organization's interest to understand the insurance requirements and procure all necessary direct and liability coverages.(...) | IEEE | 2019 | 10.1109/icws.2019.00018 | Joshi K., Joshi K.P., Mittal S. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072767819&doi=10.1109%2fICWS.2019.00018&partnerID=40&md5=9cbd97e2a8279756dad060efe5994d22 | United States | entity extraction, relation extraction, semantic search | solution proposal | method | law; information technology |
| Conference Paper | A Survey of Knowledge Reasoning Based on Kg | Inference engines; Learning algorithms; Manufacture; Natural language processing systems; Future improvements; Inference models; Knowledge graphs; Knowledge reasoning; Look-forward; NAtural language processing; Question Answering; Machine learning(...) | Knowledge Reasoning(KR) has become the core issue in the field of Artificial Intelligence(AI) and even Natural Language Processing(NLP). KR based on Knowledge Graph(KG) is based on existing KG's facts. It uses some inference models and algorithms to infer new unknown knowledge and targets at improving the completeness and accuracy of KG. This article presents a brief overview of KR based on KG, expounds the connotation and research scope of it, judges the two main research directions(Knowledge G(...) | Scopus | 2019 | 10.1088/1757-899x/569/5/052058 | Lu R., Cai Z., Zhao S. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85071851447&doi=10.1088%2f1757-899X%2f569%2f5%2f052058&partnerID=40&md5=f5eeb1c6dc1492b77694a31f594e4fa4 | China | entity classification, link prediction, question answering | secondary research | guidelines | - |
| Conference Paper | A Survey of Relation Extraction of Knowledge Graphs | Knowledge graph; Machine learning; Relation extraction(...) | With the widespread use of big data, knowledge graph has become a new hotspot. It is used in intelligent question answering, recommendation system, map navigation and so on. Constructing a knowledge graph includes ontology construction, annotated data, relation extraction, and ontology inspection. Relation extraction is to solve the problem of entity semantic linking, which is of great significance to many natural language processing applications. Research related to relation extraction has gain(...) | Scopus | 2019 | 10.1007/978-3-030-33982-1_5 | Li A., Wang X., Wang W., Zhang A., Li B. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090282440&doi=10.1007%2f978-3-030-33982-1_5&partnerID=40&md5=deeaf3415e8f4a278d70c2c69435314c | China | relation extraction | secondary research | guidelines | - |
| Conference Paper | A Task-Oriented Dialogue System for Moral Education | Dialogue system; Knowledge graph; Moral education(...) | We present a novel and practical dialogue system specifically designed for teachers and parents to solve students’ problems in moral education. Guided by the case-based reasoning theory, we collect the high-quality cases and teaching strategies from heterogeneous sources, and then construct the dedicated knowledge graph to manage the large volume of information in this domain. By leveraging on the latest natural language processing techniques, we finally implement a task-oriented dialogue system(...) | Scopus | 2019 | 10.1007/978-3-030-23207-8_72 | Peng Y., Chen P., Lu Y., Meng Q., Xu Q., Yu S. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068332818&doi=10.1007%2f978-3-030-23207-8_72&partnerID=40&md5=4278f8da26912363efe7b220b4848093 | China | conversational interfaces | solution proposal | tool | education |
| Conference Paper | A Thesaurus-Guided Method for Smart Manufacturing Diagnostics | Knowledge graph; Natural Language Processing; Smart maintenance; Thesaurus(...) | The unstructured historical data available in the databases of Computerized Maintenance Management Systems represents a wealth of diagnostic knowledge. In this paper, a methodology for converting the maintenance log data into formal knowledge graphs is presented. The methodology uses text analytics techniques, in combination with human-assisted thesaurus development methods, for generating a formal thesaurus, or knowledge graph, that encodes the semantic relationships between multiple maintenanc(...) | Scopus | 2019 | 10.1007/978-3-030-30000-5_88 | Ameri F., Yoder R. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072986239&doi=10.1007%2f978-3-030-30000-5_88&partnerID=40&md5=e000e83f56c3a1c417ad05a947540674 | United States | entity extraction, relation extraction, semantic search | solution proposal | tool | engineering |
| Conference Paper | Agrikg: an Agricultural Knowledge Graph and Its Applications | Character recognition; Database systems; Deep learning; Agricultural productions; Downstream applications; Intelligent technology; ITS applications; Knowledge graphs; Learning techniques; Question Answering; Unstructured texts; Agriculture(...) | Recently, with the development of information and intelligent technology, agricultural production and management have been significantly boosted. But it still faces considerable challenges on how to effectively integrate large amounts of fragmented information for downstream applications. To this end, in this paper, we propose an agricultural knowledge graph, namely AgriKG, to automatically integrate the massive agricultural data from internet. By applying the NLP and deep learning techniques, A(...) | Scopus | 2019 | 10.1007/978-3-030-18590-9_81 | Chen Y., Kuang J., Cheng D., Zheng J., Gao M., Zhou A. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065399556&doi=10.1007%2f978-3-030-18590-9_81&partnerID=40&md5=4b5bb7807a3da3097fe375249ed03125 | China | entity extraction, relation extraction, semantic search | solution proposal | tool | agriculture |
| Journal Article | An Automatic Literature Knowledge Graph and Reasoning Network Modeling Framework Based on Ontology and Natural Language Processing | Knowledge graph; Knowledge reasoning; Natural language processing; Representation ontology(...) | With the advancement of scientific and engineering research, a huge number of academic literature are accumulated. Manually reviewing the existing literature is the main way to explore embedded knowledge, and the process is quite time-consuming and labor intensive. As the quantity of literature is increasing exponentially, it would be more difficult to cover all aspects of the literature using the traditional manual review approach. To overcome this drawback, bibliometric analysis is used to ana(...) | ScienceDirect | 2019 | 10.1016/j.aei.2019.100959 | Chen H., Luo X. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068231884&doi=10.1016%2fj.aei.2019.100959&partnerID=40&md5=4e626a3b1b7d05ff874d8290add23a74 | China, Hong Kong | entity extraction, relation extraction, entity classification, semantic search | solution proposal | method | scholarly domain |
| Conference Paper | Application Prospect of Knowledge Graph Technology in Knowledge Management of Oil and Gas Exploration and Development | analogy and intelligent prediction; exploration and development; intelligent question and answer; knowledge graph; knowledge management; knowledge push; semantic search(...) | A large number of research reports have been produced during the exploration and development of oil and gas. Traditional relational database-based information management systems and keyword-based information retrieval systems cannot effectively analyze, organize, and utilize the knowledge in these research reports. knowledge graph use machine learning, natural language processing, semantic search and other technologies to extract knowledge from multi-source heterogeneous knowledge carriers and b(...) | IEEE | 2019 | 10.1109/icaibd.2019.8837003 | Guan Q., Zhang F., Zhang E. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073189711&doi=10.1109%2fICAIBD.2019.8837003&partnerID=40&md5=0255cd4adc82365620a9de58b5a8132d | China | entity extraction, relation extraction, entity linking, semantic search | solution proposal | tool | energy |
| Conference Paper | Application of Geocognitive Technologies to Basin & Petroleum System Analyses | Character recognition; Cognitive systems; Convolutional neural networks; Deep neural networks; Engines; Gasoline; Graph Databases; Lithology; Natural language processing systems; Ontology; Petroleum geology; Petroleum prospecting; Petroleum reservoir evaluation; Recurrent neural networks; Structural geology; Amount of information; Extracting information; Graphical representations; Innovative technology; NAtural language processing; Structural elements; Technical documents; Three-step approach; S(...) | Objectives/Scope: When dealing with new exploration areas, basin geologists face the challenge of collecting relevant information from all available sources. This include a number of structured commercial databases, but also large corpora of technical documents in which an invaluable amount of information is scattered across. Even if assisted by search tools to filter the documents of interest, extracting information requires a human effort in reading and understanding the documents. Methods, Pr(...) | Scopus | 2019 | 10.2118/197610-ms | Ruffo P., Piantanida M., Bergero F., Staar P., Bekas C. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084780600&doi=10.2118%2f197610-ms&partnerID=40&md5=14ea728e8b5ffee8889ac775de259b50 | Italy, United States | entity extraction, relation extraction, ontology construction, semantic search | solution proposal | tool | natural science; energy |
| Conference Paper | Artificial Intelligence for the Early Design Phases of Space Missions | Concurrent engineering; Data handling; Economic and social effects; Engines; Expert systems; Knowledge management; Knowledge representation; Learning algorithms; Learning systems; Life cycle; Natural language processing systems; Network architecture; Ontology; Space flight; User interfaces; European Space Agency; Human-machine interaction; Knowledge representation and reasoning; Model-based system engineerings; Multi word extraction; NAtural language processing; Searching for informations; Word (...) | Recent introduction of data mining methods has led to a paradigm shift in the way we can analyze space data. This paper demonstrates that Artificial Intelligence (AI), and especially the field of Knowledge Representation and Reasoning (KRR), could also be successfully employed at the start of the space mission life cycle via an Expert System (ES) used as a Design Engineering Assistant (DEA). An ES is an AI-based agent used to solve complex problems in particular fields. There are many examples o(...) | IEEE | 2019 | 10.1109/aero.2019.8742082 | Berquand A., Murdaca F., Riccardi A., Soares T., Generé S., Brauer N., Kumar K. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068345246&doi=10.1109%2fAERO.2019.8742082&partnerID=40&md5=d2f4d8a6525558a62ed49c0c8931a394 | Germany, United Kingdom, Netherlands | entity extraction, relation extraction, ontology construction | solution proposal | tool | engineering |
| Conference Paper | Assessing the Lexico-Semantic Relational Knowledge Captured by Word and Concept Embeddings | Embedding evaluation; Knowledge graphs; Lexico-semantic relations(...) | Deep learning currently dominates the benchmarks for various NLP tasks and, at the basis of such systems, words are frequently represented as embeddings - vectors in a low dimensional space - learned from large text corpora and various algorithms have been proposed to learn both word and concept embeddings. One of the claimed benefits of such embeddings is that they capture knowledge about semantic relations. Such embeddings are most often evaluated through tasks such as predicting human-rated s(...) | ACM | 2019 | 10.1145/3360901.3364445 | Denaux R., Gomez-Perez J.M. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077265357&doi=10.1145%2f3360901.3364445&partnerID=40&md5=15a0f51088a9f2a6f9e2afd6ae018b56 | Spain | relation classification, knowledge graph embedding | validation research | method | - |
| Conference Paper | Augmenting Named Entity Recognition with Commonsense Knowledge | - | Commonsense can be vital in some applications like Natural Language Understanding (NLU), where it is often required to resolve ambiguity arising from implicit knowledge and underspecification. In spite of the remarkable success of neural network approaches on a variety of Natural Language Processing tasks, many of them struggle to react effectively in cases that require commonsense knowledge. In the present research, we take advantage of the availability of the open multilingual knowledge graph (...) | ACL | 2019 | - | Dekhili, Gaith and Le, Tan Ngoc and Sadat, Fatiha | https://aclanthology.org/W19-3644 | Canada | augmented language models, entity extraction | solution proposal | technique | - |
| Conference Paper | Automated Event Extraction Model for Linked Portuguese Documents | Data mining; Learning systems; Natural language processing systems; Event extraction; Knowledge graphs; Named entities; Ontological structures; Question Answering; Sparql queries; Extraction(...) | In recent times, Machine Learning is booming and researchers are applying it to the most conceivable cases such as the area of linked documents. This article presents a process of automatic event extraction from Portuguese linked document whose accuracy (95.00%) was calculated by manual verification. With the help of an ontological structure, extracted events are mapped as a knowledge graph that represents the named entities and the events associated with each document. Such graphs are accessibl(...) | Scopus | 2019 | - | Kashyap R., Teresa G., Paulo Q., Beires Nogueira V. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85066468101&partnerID=40&md5=500890662a4847a6b58ea9ca396e6d6d | Portugal | entity extraction | validation research | method | - |
| Conference Paper | Automatic Analysis and Reasoning Based on Vulnerability Knowledge Graph | Cybersecurity; Knowledge extraction; Knowledge graph; Knowledge graph reasoning; Vulnerability(...) | In the security community, it is valuable to extract and store the vulnerability knowledge. Many data sources record vulnerability in unstructured data and semi-structured data which are hard for machine-understanding and reuse. Security expert need to analyze the description, link to related knowledge and reason out the hidden connection among various weakness. It is necessary to analyze the vulnerability data automatically and manage knowledge in a more intelligent method. In this paper, we pr(...) | Scopus | 2019 | 10.1007/978-981-15-1922-2_1 | Qin S., Chow K.P. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076971573&doi=10.1007%2f978-981-15-1922-2_1&partnerID=40&md5=6ee331224995e0d86a8fc1742a6131cc | Hong Kong | entity extraction, entity linking | solution proposal | method; resource | information technology |
| Journal Article | Beyond Word Embeddings: Learning Entity and Concept Representations from Large Scale Knowledge Bases | Concept categorization; Entity and concept embeddings; Entity identification; Knowledge graph representations; Probase; Skip-gram(...) | Text representations using neural word embeddings have proven effective in many NLP applications. Recent researches adapt the traditional word embedding models to learn vectors of multiword expressions (concepts/entities). However, these methods are limited to textual knowledge bases (e.g., Wikipedia). In this paper, we propose a novel and simple technique for integrating the knowledge about concepts from two large scale knowledge bases of different structure (Wikipedia and Probase) in order to (...) | Scopus | 2019 | 10.1007/s10791-018-9340-3 | Shalaby W., Zadrozny W., Jin H. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85052060481&doi=10.1007%2fs10791-018-9340-3&partnerID=40&md5=e1915bd676ccbb126a6dd18dd829ad61 | United States | augmented language models, knowledge graph embedding | validation research | technique | - |
| Conference Paper | Con2Kg-A Large-Scale Domain-Specific Knowledge Graph | knowledge graph, recruitment domain, natural language processing(...) | This paper presents Con2KG, a large-scale recruitment domain Knowledge Graph that describes 4 million triples as facts from 250 thousands of unstructured data of job postings. We propose a novel framework for Knowledge Graph construction from unstructured text and an unsupervised, dynamically evolving ontology that helps Con2KG to capture hierarchical links between the entities missed by explicit relational facts in the triples. To enrich our graph, we include entity context and its polarity. To(...) | ACM | 2019 | 10.1145/3342220.3344931 | Goyal, Nidhi and Sachdeva, Niharika and Choudhary, Vijay and Kar, Rijula and Kumaraguru, Ponnurangam and Rajput, Nitendra | https://doi.org/10.1145/3342220.3344931 | India | entity extraction, relation extraction | solution proposal | tool | business |
| Conference Paper | Conceptualisation and Annotation of Drug Nonadherence Information for Knowledge Extraction from Patient-Generated Texts | Computational linguistics; Data mining; Extraction; Natural language processing systems; Annotation scheme; Drug effects; Extraction systems; Knowledge extraction; Named entities; Noun phrase; Scale-up; Systems trainings; Train systems; User-generated; Knowledge graph(...) | Approaches to knowledge extraction (KE) in the health domain often start by annotating text to indicate the knowledge to be extracted, and then use the annotated text to train systems to perform the KE. This may work for annotating named entities or other contiguous noun phrases (drugs, some drug effects), but becomes increasingly difficult when items tend to be expressed across multiple, possibly noncontiguous, syntactic constituents (e.g. most descriptions of drug effects in user-generated tex(...) | ACL | 2019 | - | Belz A., Ford E., Hoile R., Mullick A. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107398817&partnerID=40&md5=607c7acfe33177c692ad5260b7f342a6 | United Kingdom | entity extraction, relation extraction | solution proposal | method | health |
| Conference Paper | Construction Research and Application of Poverty Alleviation Knowledge Graph | An approach of knowledge graph construction; Bayesian classification; Knowledge question answering; Neo4j graph storage; Poverty alleviation knowledge graph(...) | Based on the integration of multi-source data, an approach of domain-specific knowledge graph construction is proposed to guide the construction of a “people-centered” poverty alleviation knowledge graph, and to achieve cross-functional and cross-regional sharing and integration of national basic data resources and public services. Focusing on “precise governance and benefit people service”, poverty alleviation ontology is constructed to solve semantic heterogeneity in multiple data sources inte(...) | Scopus | 2019 | 10.1007/978-3-030-30952-7_42 | Yun H., He Y., Lin L., Pan Z., Zhang X. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075604976&doi=10.1007%2f978-3-030-30952-7_42&partnerID=40&md5=ad05e8ecb90d9d804a3578be64bf8330 | China | entity extraction, relation extraction, semantic search, ontology construction | solution proposal | tool | public sector |
| Journal Article | Construction of an Industrial Knowledge Graph for Unstructured Chinese Text Learning | Deep learning; Industrial big data; Industrial knowledge graph; Industry 4.0; Intellectualization of industrial information; Social network(...) | The industrial 4.0 era is the fourth industrial revolution and is characterized by network penetration; therefore, traditional manufacturing and value creation will undergo revolutionary changes. Artificial intelligence will drive the next industrial technology revolution, and knowledge graphs comprise the main foundation of this revolution. The intellectualization of industrial information is an important part of industry 4.0, and we can efficiently integrate multisource heterogeneous industria(...) | Scopus | 2019 | 10.3390/app9132720 | Zhao M., Wang H., Guo J., Liu D., Xie C., Liu Q., Cheng Z. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072242633&doi=10.3390%2fAPP9132720&partnerID=40&md5=6d0643c1ddf245c6c8c1dcfe7cb02a4f | China | entity extraction, relation extraction | validation research | method | engineering |
| Conference Paper | Cracking the Contextual Commonsense Code: Understanding Commonsense Reasoning Aptitude of Deep Contextual Representations | - | Pretrained deep contextual representations have advanced the state-of-the-art on various commonsense NLP tasks, but we lack a concrete understanding of the capability of these models. Thus, we investigate and challenge several aspects of BERT{'}s commonsense representation abilities. First, we probe BERT{'}s ability to classify various object attributes, demonstrating that BERT shows a strong ability in encoding various commonsense features in its embedding space, but is still deficient in many (...) | ACL | 2019 | 10.18653/v1/d19-6001 | Da, Jeff and Kasai, Jungo | https://aclanthology.org/D19-6001 | United States | augmented language models, natural language inference | validation research | technique | - |
| Conference Paper | Difficulties and Improvements to Graph-Based Lexical Sentiment Analysis Using Lisa | Sentiment Analysis, Affect Analysis, Knowledge Base, Graph Navigation, Sentiment Lexicon, ANEW(...) | Lexical sentiment analysis (LSA) underlines a family of methods combining natural language processing, machine learning, or graph navigation techniques to identify the underlying sentiments or emotions carried in textual data. In this paper, we introduce LISA, an unsupervised word-level knowledge graph-based LexIcal Sentiment Analysis framework. It uses different variants of shortest path graph navigation techniques to compute and propagate affective scores in a lexical-Affective graph (LAG), cr(...) | IEEE | 2019 | 10.1109/iccc.2019.00008 | Fares M., Moufarrej A., Jreij E., Tekli J., Grosky W. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072793727&doi=10.1109%2fICCC.2019.00008&partnerID=40&md5=7be1b4aa6806df28bfcdeb1c5073483b | Lebanon, United States | text analysis | validation research | tool | - |
| Conference Paper | Difficulty-Controllable Multi-Hop Question Generation from Knowledge Graphs | Knowledge graph; Natural language processing; Neural network; Question generation; Transformer(...) | Knowledge graphs have become ubiquitous data sources and their utility has been amplified by the research on ability to answer carefully crafted questions over knowledge graphs. We investigate the problem of question generation (QG) over knowledge graphs wherein, the level of difficulty of the question can be controlled. We present an end-to-end neural network-based method for automatic generation of complex multi-hop questions over knowledge graphs. Taking a subgraph and an answer as input, our(...) | Scopus | 2019 | 10.1007/978-3-030-30793-6_22 | Kumar V., Hua Y., Ramakrishnan G., Qi G., Gao L., Li Y.-F. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075720005&doi=10.1007%2f978-3-030-30793-6_22&partnerID=40&md5=9ef13d2dfba894395c43d0e9965d082b | Australia, China, India | question generation, question answering | validation research | tool; resource | - |
| Conference Paper | Edgegat: an Approach to Add External Knowledge for Semantic Matching | Knowledge graphs; Natural language inference; Natural language processing(...) | Natural Language Inference (NLI) is one of NLP tasks to deduce, given a premise, whether a relevant hypothesis should be declared true or false. In view of the performance of previous models, the improvement provided by external knowledge is substantial. Inspired by it, we propose a new mechanism for introducing external knowledge, i.e., adding the graph convolutional network (EDGEGAT) we designed to the NLI model. Unlike previous external knowledge methods, EDGEGAT can easily be combined with N(...) | IEEE | 2019 | 10.1109/iccsnt47585.2019.8962429 | Song M., Zhao W., Haihong E. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079101339&doi=10.1109%2fICCSNT47585.2019.8962429&partnerID=40&md5=89ce3503ad982dde7ef35b8ada21e20c | China | natural language inference | validation research | technique | - |
| Journal Article | Embedding Learning with Triple Trustiness on Noisy Knowledge Graph | Cross entropy; Embedding learning; Knowledge graph; Noise detection; Triple trustiness(...) | Embedding learning on knowledge graphs (KGs) aims to encode all entities and relationships into a continuous vector space, which provides an effective and flexible method to implement downstream knowledge-driven artificial intelligence (AI) and natural language processing (NLP) tasks. Since KG construction usually involves automatic mechanisms with less human supervision, it inevitably brings in plenty of noises to KGs. However, most conventional KG embedding approaches inappropriately assume th(...) | Scopus | 2019 | 10.3390/e21111083 | Zhao Y., Feng H., Gallinari P. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075465861&doi=10.3390%2fe21111083&partnerID=40&md5=b36481ea4e03b9b6fc51e0dacbd942a5 | China, France | knowledge graph embedding | validation research | technique | - |
| Journal Article | Embedding Logic Rules into Recurrent Neural Networks | logic rules; named entity recognition; RNN; sentiment classification(...) | Incorporating prior knowledge into recurrent neural network (RNN) is of great importance for many natural language processing tasks. However, most of the prior knowledge is in the form of structured knowledge and is difficult to be exploited in the existing RNN framework. By extracting the logic rules from the structured knowledge and embedding the extracted logic rule into the RNN, this paper proposes an effective framework to incorporate the prior information in the RNN models. First, we demon(...) | IEEE | 2019 | 10.1109/access.2019.2892140 | Chen B., Hao Z., Cai X., Cai R., Wen W., Zhu J., Xie G. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061738019&doi=10.1109%2fACCESS.2019.2892140&partnerID=40&md5=406e85a9e3261688e498aef73d7594e7 | China | augmented language models | validation research | method | - |
| Conference Paper | Enabling Search and Collaborative Assembly of Causal Interactions Extracted from Multilingual and Multi-Domain Free Text | Climate change; Computational linguistics; Food supply; Collaborative assembly; Domain machines; Food security; Knowledge graphs; Multi-disciplinary collaborations; Multiple languages; Research problems; Scientific information; Data mining(...) | Many of the most pressing current research problems (e.g., public health, food security, or climate change) require multi-disciplinary collaborations. In order to facilitate this process, we propose a system that incorporates multidomain extractions of causal interactions into a single searchable knowledge graph. Our system enables users to search iteratively over direct and indirect connections in this knowledge graph, and collaboratively build causal models in real time. To enable the aggregat(...) | ACL | 2019 | - | Barbosa G.C.G., Wong Z., Hahn-Powell G., Bell D., Sharp R., Valenzuela-Escarcega M.A., Surdeanu M. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085641989&partnerID=40&md5=6d5935ab7701707075be83b7e9341332 | Brazil, United States | semantic search | solution proposal | tool | scholarly domain |
| Conference Paper | Engineering Knowledge Graph for Keyword Discovery in Patent Search | Engineering knowledge graph; Machine learning; Ontologies; Semantic data processing(...) | Patent retrieval and analytics have become common tasks in engineering design and innovation. Keyword-based search is the most common method and the core of integrative methods for patent retrieval. Often searchers intuitively choose keywords according to their knowledge on the search interest which may limit the coverage of the retrieval. Although one can identify additional keywords via reading patent texts from prior searches to refine the query terms heuristically, the process is tedious, ti(...) | Scopus | 2019 | 10.1017/dsi.2019.231 | Sarica S., Song B., Low E., Luo J. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076468727&doi=10.1017%2fdsi.2019.231&partnerID=40&md5=46825280606b569a78765523b1d07f04 | Singapore | semantic search | solution proposal | tool; resource | law |
| Conference Paper | Eoann: Lexical Semantic Relation Classification Using an Ensemble of Artificial Neural Networks | - | Researchers use wordnets as a knowledge base in many natural language processing tasks and applications, such as question answering, textual entailment, discourse classification, and so forth. Lexico-semantic relations among words or concepts are important parts of knowledge encoded in wordnets. As the use of wordnets becomes extensively widespread, extending the existing ones gets more attention. Manually construction and extension of lexico-semantic relations for WordNets or knowledge graphs a(...) | ACL | 2019 | 10.26615/978-954-452-056-4_057 | Hosseini Pour, Rayehe and Shamsfard, Mehrnoush | https://aclanthology.org/R19-1057 | Iran | relation classification | validation research | technique | - |
| Conference Paper | Ernie: Enhanced Language Representation with Informative Entities | - | Neural language representation models such as BERT pre-trained on large-scale corpora can well capture rich semantic patterns from plain text, and be fine-tuned to consistently improve the performance of various NLP tasks. However, the existing pre-trained language models rarely consider incorporating knowledge graphs (KGs), which can provide rich structured knowledge facts for better language understanding. We argue that informative entities in KGs can enhance language representation with exter(...) | ACL | 2019 | 10.18653/v1/p19-1139 | Zhang, Zhengyan and Han, Xu and Liu, Zhiyuan and Jiang, Xin and Sun, Maosong and Liu, Qun | https://aclanthology.org/P19-1139 | China | augmented language models | validation research | tool | - |
| Conference Paper | Extending Cross-Domain Knowledge Bases with Long Tail Entities Using Web Table Data | Data integration; Database systems; Knowledge based systems; Natural language processing systems; Sports; Back-ground knowledge; Football players; Knowledge basis; Knowledge graphs; Natural language understanding; Question Answering; Schema matching; Unknown entities; Search engines(...) | Cross-domain knowledge bases such as YAGO, DBpedia, or the Google Knowledge Graph are being used as background knowledge within an increasing range of applications including web search, data integration, natural language understanding, and question answering. The usefulness of a knowledge base for these applications depends on its completeness. Relational HTML tables from the Web cover a wide range of topics and describe very specific long tail entities, such as small villages, less-known footba(...) | Scopus | 2019 | 10.5441/002/edbt.2019.34 | Oulabi Y., Bizer C. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85064894038&doi=10.5441%2f002%2fedbt.2019.34&partnerID=40&md5=bf85bd3debd53a39e154cd95e12ea48c | Germany | entity extraction, entity classification | validation research | method | sports |
| Conference Paper | Fake News Detection Via Nlp Is Vulnerable to Adversarial Attacks | Attack; Fact Checking; Fake News Detection; NLP; Outsourced Knowledge Graph(...) | News plays a significant role in shaping people's beliefs and opinions. Fake news has always been a problem, which wasn't exposed to the mass public until the past election cycle for the 45th President of the United States. While quite a few detection methods have been proposed to combat fake news since 2015, they focus mainly on linguistic aspects of an article without any fact checking. In this paper, we argue that these models have the potential to misclassify fact-tampering fake news as well(...) | Scopus | 2019 | 10.5220/0007566307940800 | Zhou Z., Guan H., Bhat M.M., Hsu J. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85064814138&doi=10.5220%2f0007566307940800&partnerID=40&md5=375c826442cc6befdf27ad0b2e0fe4ce | China, United States | text analysis | solution proposal | guidelines | news |
| Conference Paper | Foodkg: a Semantics-Driven Knowledge Graph for Food Recommendation | HTTP; Natural language processing systems; Cognitive agents; Construction process; Health condition; Knowledge graphs; Natural language questions; Software toolkits; Semantic Web(...) | The proliferation of recipes and other food information on the Web presents an opportunity for discovering and organizing diet-related knowledge into a knowledge graph. Currently, there are several ontologies related to food, but they are specialized in specific domains, e.g., from an agricultural, production, or specific health condition point-of-view. There is a lack of a unified knowledge graph that is oriented towards consumers who want to eat healthily, and who need an integrated food sugge(...) | Scopus | 2019 | 10.1007/978-3-030-30796-7_10 | Haussmann S., Seneviratne O., Chen Y., Ne’eman Y., Codella J., Chen C.-H., McGuinness D.L., Zaki M.J. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081079416&doi=10.1007%2f978-3-030-30796-7_10&partnerID=40&md5=10c19929136f853470c4d5eb27dd77af | United States | entity extraction, relation extraction, ontology construction, question answering | validation research | tool; resource | food |
| Conference Paper | Framework for Question-Answering in Sanskrit through Automated Construction of Knowledge Graphs | - | Sanskrit (samskrta) enjoys one of the largest and most varied literature in the whole world. Extracting the knowledge from it, however, is a challenging task due to multiple reasons including complexity of the language and paucity of standard natural language processing tools. In this paper, we target the problem of building knowledge graphs for particular types of relationships from samskrta texts. We build a natural language question-answering system in samskrta that uses the knowledge graph t(...) | ACL | 2019 | - | Terdalkar H,Bhattacharya A | https://aclanthology.org/W19-7508.pdf | India | question answering | solution proposal | method | - |
| Conference Paper | Generating Knowledge Graph Paths from Textual Definitions Using Sequence-To-Sequence Models | Computational linguistics; Mapping; Knowledge graphs; Mapping systems; Model outputs; Proof of concept; Sequence models; State-of-the-art system; Structured prediction; Unrestricted texts; Graph theory(...) | We present a novel method for mapping unrestricted text to knowledge graph entities by framing the task as a sequence-to-sequence problem. Specifically, given the encoded state of an input text, our decoder directly predicts paths in the knowledge graph, starting from the root and ending at the target node following hypernym-hyponym relationships. In this way, and in contrast to other text-to-entity mapping systems, our model outputs hierarchically structured predictions that are fully interpret(...) | ACL | 2019 | - | Prokhorov V., Pilehvar M.T., Collier N. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085580987&partnerID=40&md5=75fa179b9d98094c591880397d81fb45 | United Kingdom | entity linking, link prediction | validation research | technique | - |
| Conference Paper | Grapal: Connecting the Dots in Scientific Literature | - | We introduce GrapAL (Graph database of Academic Literature), a versatile tool for exploring and investigating a knowledge base of scientific literature that was semi-automatically constructed using NLP methods. GrapAL fills many informational needs expressed by researchers. At the core of GrapAL is a Neo4j graph database with an intuitive schema and a simple query language. In this paper, we describe the basic elements of GrapAL, how to use it, and several use cases such as finding experts on a (...) | ACL | 2019 | 10.18653/v1/p19-3025 | Betts, Christine and Power, Joanna and Ammar, Waleed | https://aclanthology.org/P19-3025 | United States | entity extraction, relation extraction, semantic search | solution proposal | tool | scholarly domain |
| Journal Article | Graph Convolutional Network with Sequential Attention for Goal-Oriented Dialogue Systems | - | Domain-specific goal-oriented dialogue systems typically require modeling three types of inputs, namely, (i) the knowledge-base associated with the domain, (ii) the history of the conversation, which is a sequence of utterances, and (iii) the current utterance for which the response needs to be generated. While modeling these inputs, current state-of-the-art models such as Mem2Seq typically ignore the rich structure inherent in the knowledge graph and the sentences in the conversation context. I(...) | ACL | 2019 | 10.1162/tacl_a_00284 | Banerjee, Suman and Khapra, Mitesh M. | https://aclanthology.org/Q19-1034 | India | conversational interfaces, augmented language models | validation research | tool | - |
| Conference Paper | Hierarchical Ontology Graph for Solving Semantic Issues in Decision Support Systems | Decision support systems; Knowledge graph; Neural-symbolic integration; NLP; Ontology graph; Semantic composition(...) | In the context of the development of AI algorithms in natural language processing, tremendous progress has been made in knowledge abstraction and semantic reasoning. However, for answering the questions with complex logic, AI system is still in an early stage. Hierarchical ontology graph is proposed to establish analysis threads for the complex question in order to facilitate AI system to further support in business decision making. The study of selecting the appropriate corpora is intended to i(...) | Scopus | 2019 | 10.5220/0007769904830487 | Guo H., Liu K. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067517322&doi=10.5220%2f0007769904830487&partnerID=40&md5=a64e2a8ca25263f2765e6651ad2fcab4 | United Kingdom | semantic search | solution proposal | guidelines | - |
| Conference Paper | Implementation of Intelligent Question Answering System Based on Basketball Knowledge Graph | knowledge graph; NBA; question and answer(...) | Currently most search engines query based on keywords or question-template matching. But for the retrieval about basketball or NBA, there are always too many feedback results, low accuracy and lack of intelligence. In this paper, an intelligent question answering system based on NBA basketball knowledge graph is implemented. Some methods are used in the question analysis module in the system, including question similarity calculation, named entity recognition, entity similarity calculation, and (...) | IEEE | 2019 | 10.1109/iaeac47372.2019.8997747 | Li Y., Cao J., Wang Y. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081172990&doi=10.1109%2fIAEAC47372.2019.8997747&partnerID=40&md5=1d65134ce3afda9c04939a0ba7853928 | China | question answering | solution proposal | method | sports |
| Conference Paper | Improving Named Entity Recognition with Commonsense Knowledge Pre-Training | Commonsense; ConceptNet; Deep neural networks; Word embeddings(...) | Commonsense can be vital in some applications like Natural Language Understanding, where it is often required to resolve ambiguity arising from implicit knowledge and under-specification. In spite of the remarkable success of neural network approaches on a variety of Natural Language Processing tasks, many of them struggle to react effectively in cases that require commonsense knowledge. In the present research paper, we take advantage of the availability of the open multilingual knowledge graph(...) | Scopus | 2019 | 10.1007/978-3-030-30639-7_2 | Dekhili G., Le N.T., Sadat F. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072853690&doi=10.1007%2f978-3-030-30639-7_2&partnerID=40&md5=c11be8283c4bb247e076e2b193ba4c04 | Canada | entity extraction | validation research | technique | - |
| Conference Paper | Improving the Quality and Efficiency of Operational Planning and Risk Management with Ml and Nlp | Knowledge management; Offshore oil well production; Planning; Risk management; Concurrent activities; NAtural language processing; Natural language understanding; Operation conditions; Operational experience; Operational planning; Personal experience; Technical conditions; Natural language processing systems(...) | To ensure safe and efficient operations, all offshore operations follow a plan devised to take into account current operation conditions and identify the optimum workflow with the minimum risk potential. Previously, planners had to manually consult eight data sources, each with a separate UI, and summarise the plan in a.pdf document. Equinor's Operation Planning Tool (OPT) has been developed to easily present the planners with the technical conditions of a platform, identify potentially dangerou(...) | Scopus | 2019 | 10.2118/195750-ms | Birnie C.E., Sampson J., Sjaastad E., Johansen B., Obrestad L.E., Larsen R., Khamassi A. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084012858&doi=10.2118%2f195750-MS&partnerID=40&md5=0a184d1d1db50ac4d435ae8e5b6b32a0 | United Kingdom | entity extraction, relation extraction, ontology construction, semantic search | evaluation research | tool | energy |
| Conference Paper | Incorporating Syntactic and Semantic Information in Word Embeddings Using Graph Convolutional Networks | - | Word embeddings have been widely adopted across several NLP applications. Most existing word embedding methods utilize sequential context of a word to learn its embedding. While there have been some attempts at utilizing syntactic context of a word, such methods result in an explosion of the vocabulary size. In this paper, we overcome this problem by proposing SynGCN, a flexible Graph Convolution based method for learning word embeddings. SynGCN utilizes the dependency context of a word without (...) | ACL | 2019 | 10.18653/v1/p19-1320 | Vashishth, Shikhar and Bhandari, Manik and Yadav, Prateek and Rai, Piyush and Bhattacharyya, Chiranjib and Talukdar, Partha | https://aclanthology.org/P19-1320 | India | augmented language models | validation research | tool | - |
| Conference Paper | Integrating Semantic Knowledge to Tackle Zero-Shot Text Classification | Character recognition; Computational linguistics; Information retrieval systems; Semantics; Text processing; Class hierarchies; Classification tasks; Data augmentation; General knowledge; Overall accuracies; Semantic knowledge; Text classification; Training data; Classification (of information)(...) | Insufficient or even unavailable training data of emerging classes is a big challenge of many classification tasks, including text classification. Recognising text documents of classes that have never been seen in the learning stage, so-called zero-shot text classification, is therefore difficult and only limited previous works tackled this problem. In this paper, we propose a two-phase framework together with data augmentation and feature augmentation to solve this problem. Four kinds of semant(...) | ACL | 2019 | - | Zhang J., Lertvittayakumjorn P., Guo Y. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084312963&partnerID=40&md5=31fda81739e5648fc3effe72b16c09f9 | United Kingdom | text classification | validation research | tool | - |
| Conference Paper | Integration of Knowledge Graph Embedding into Topic Modeling with Hierarchical Dirichlet Process | Computational linguistics; Embeddings; Information retrieval systems; Knowledge management; Bayesian nonparametric modeling; Document Classification; Hierarchical Dirichlet process; Hierarchical dirichlet process (HDP); Integration of knowledge; Large document corpora; Low-dimensional representation; Variational inference methods; Classification (of information)(...) | Leveraging domain knowledge is an effective strategy for enhancing the quality of inferred low-dimensional representations of documents by topic models. In this paper, we develop topic modeling with knowledge graph embedding (TMKGE), a Bayesian nonparametric model to employ knowledge graph (KG) embedding in the context of topic modeling, for extracting more coherent topics. Specifically, we build a hierarchical Dirichlet process (HDP) based model to flexibly borrow information from KG to improve(...) | ACL | 2019 | - | Li D., Dadaneh S.Z., Zhang J., Li P. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85071153019&partnerID=40&md5=ee9ffcf1663cd95d39e3c0878e18d238 | United States | augmented language models, text analysis, knowledge graph embedding | validation research | technique | - |
| Journal Article | Interactive Natural Language Question Answering over Knowledge Graphs | Interactive query; Knowledge graph; Natural language question and answering; Question understanding(...) | As many real-world data are constructed into knowledge graphs, providing effective and convenient query techniques for end users is an urgent and important task. Although structured query languages, such as SPARQL, offer a powerful expression ability to query RDF datasets, they are difficult to use. Keywords are simple but have a very limited expression ability. Natural language question (NLQ) is promising for querying knowledge graphs. A huge challenge is how to understand the question clearly (...) | ScienceDirect | 2019 | 10.1016/j.ins.2018.12.032 | Zheng W., Cheng H., Yu J.X., Zou L., Zhao K. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85059296689&doi=10.1016%2fj.ins.2018.12.032&partnerID=40&md5=c0f1122b7a2ef491a4a36e2de6634c9d | China, Hong Kong | question answering | validation research | technique | - |
| Conference Paper | Joint Semantic and Distributional Word Representations with Multi-Graph Embeddings | - | Word embeddings continue to be of great use for NLP researchers and practitioners due to their training speed and easiness of use and distribution. Prior work has shown that the representation of those words can be improved by the use of semantic knowledge-bases. In this paper we propose a novel way of combining those knowledge-bases while the lexical information of co-occurrences of words remains. It is conceptually clear, as it consists in mapping both distributional and semantic information i(...) | ACL | 2019 | 10.18653/v1/d19-5314 | Daix-Moreux, Pierre and Gall{'e}, Matthias | https://aclanthology.org/D19-5314 | France | augmented language models, knowledge graph embedding | validation research | technique | - |
| Conference Paper | Knowledge Extraction and Applications Utilizing Context Data in Knowledge Graphs | - | Context is widely considered for NLP and knowledge discovery since it highly influences the exact meaning of natural language. The scientific challenge is not only to extract such context data, but also to store this data for further NLP approaches. Here, we propose a multiple step knowledge graph-based approach to utilize context data for NLP and knowledge expression and extraction. We introduce the graph-theoretic foundation for a general context concept within semantic networks and show a pro(...) | WoS | 2019 | 10.15439/2019f3 | Doerpinghaus J,Stefan A | http://dx.doi.org/10.15439/2019F3 | Germany | semantic search | solution proposal | tool | - |
| Conference Paper | Knowledge Graph Based Learning Guidance for Cybersecurity Hands-On Labs | Cybersecurity; Knowledge graph; Laboratory(...) | Hands-on practice is a critical component of cybersecurity education. Most of the existing hands-on exercises or labs materials are usually managed in a problem-centric fashion, while it lacks a coherent way to manage existing labs and provide productive lab exercising plans for cybersecurity learners. With the advantages of big data and natural language processing (NLP) technologies, constructing a large knowledge graph and mining concepts from unstructured text becomes possible, which motivate(...) | ACM | 2019 | 10.1145/3300115.3309531 | Deng Y., Lu D., Huang D., Chung C.-J., Lin F. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065997977&doi=10.1145%2f3300115.3309531&partnerID=40&md5=00a70d6ba705828ff40a685447a16c56 | United States | entity alignment, semantic search | solution proposal | tool | education |
| Conference Paper | Knowledge-Aware Textual Entailment with Graph Attention Network | Graph attention network; Knowledge base; Textual entailment(...) | Textual entailment is a central problem of language variability, which has been attracting a lot of interest and it poses significant issues in front of systems aimed at natural language understanding. Recently, various frameworks have been proposed for textual entailment recognition, ranging from traditional computational linguistics techniques to deep learning model based methods. However, recent deep neural networks that achieve the state of the art on textual entailment task only consider th(...) | ACM | 2019 | 10.1145/3357384.3358071 | Chen D., Li Y., Yang M., Zheng H.-T., Shen Y. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075468679&doi=10.1145%2f3357384.3358071&partnerID=40&md5=122e9e69faf8c86a7cfce803b0cf3b2e | China, United States | natural language inference | validation research | technique | - |
| Conference Paper | Knowledge-Enhanced Ensemble Learning for Word Embeddings | Ensemble model; Knowledge graph; Word embedding(...) | Representing words as embeddings in a continuous vector space has been proven to be successful in improving the performance in many natural language processing (NLP) tasks. Beyond the traditional methods that learn the embeddings from large text corpora, ensemble methods have been proposed to leverage the merits from pre-trained word embeddings as well as external semantic sources. In this paper, we propose a knowledge-enhanced ensemble method to combine both knowledge graphs and pre-trained wor(...) | ACM | 2019 | 10.1145/3308558.3313425 | Fang L., Luo Y., Feng K., Zhao K., Hu A. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85066898567&doi=10.1145%2f3308558.3313425&partnerID=40&md5=6c9f65c8d066edfe67223970d2835676 | China, Singapore | augmented language models | validation research | tool | - |
| Conference Paper | Learning Embeddings from Scientific Corpora Using Lexical, Grammatical and Semantic Information | Convolutional neural networks; Embeddings; Neural networks; NLP; Text classification(...) | Natural language processing can assist scientists to leverage the increasing amount of information contained in scientific bibliography. The current trend, based on deep learning and embeddings, uses representations at the (sub)word level that require large amounts of training data and neural architectures with millions of parameters to learn successful language models, like BERT. However, these representations may not be well suited for the scientific domain, where it is common to find complex (...) | Scopus | 2019 | - | Garcia-Silva A., Denaux R., Gomez-Perez J.M. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077823532&partnerID=40&md5=532805ed6a552718a6e8de3f1b4c3abc | Spain | augmented language models, text classification | validation research | technique | scholarly domain |
| Conference Paper | Leveraging Domain Context for Question Answering over Knowledge Graph | Big data; Natural language processing systems; Semantics; Complex questions; Domain knowledge; Information interaction; Knowledge graphs; New approaches; Question Answering; Real data sets; Semantic parsing; Knowledge management(...) | This paper focuses on the problem of question answering over knowledge graph (KG-QA). With the increasing availability of different knowledge graphs in a variety of domains, KG-QA becomes a prevalent information interaction approach. Current KG-QA methods usually resort to semantic parsing, retrieval or neural matching based models. However, current methods generally ignore the rich domain context, i.e., category and surrounding descriptions within the knowledge graphs. Experiments shows that th(...) | Scopus | 2019 | 10.1007/978-3-030-26072-9_27 | Tong P., Yao J., He L., Xu L. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85070012145&doi=10.1007%2f978-3-030-26072-9_27&partnerID=40&md5=83103122ed6026651a53102d473bc7d3 | China | question answering | validation research | method | - |
| Conference Paper | Leveraging Knowledge Graph for Open-Domain Question Answering | Automated Question Answering; Diffbot Knowledge Graph; Information Retrieval; Natural Language Processing(...) | Rich and comprehensive knowledge graphs (KG) of the Web, such as, Google KG, NELL, and Diffbot KG, are becoming increasingly prevalent and powerful as the underlying AI technology is rapidly progressing. In this work, we leverage this ongoing advancement for the task of answering questions posed from any domain and any type (factoid and non-factoid). We present a framework for knowledge graph based question answering systems, KGQA, and experiment with an instance of this framework that employs D(...) | IEEE | 2019 | 10.1109/wi.2018.00-63 | Ortiz Costa J., Kulkarni A. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061928044&doi=10.1109%2fWI.2018.00-63&partnerID=40&md5=a5c9365ae0c71db1a0eff53f09a2afa8 | United States | question answering | validation research | method | - |
| Conference Paper | Leveraging Lexical Semantic Information for Learning Concept-Based Multiple Embedding Representations for Knowledge Graph Completion | Concept information; Knowledge graph completion; Representation learning(...) | Knowledge graphs (KGs) are important resources for a variety of natural language processing tasks but suffer from incompleteness. To address this challenge, a number of knowledge graph completion (KGC) methods have been developed using low-dimensional graph embeddings. Most existing methods focus on the structured information of triples in encyclopaedia KG and maximize the likelihood of them. However, they neglect semantic information contained in lexical KG. To overcome this drawback, we propos(...) | Scopus | 2019 | 10.1007/978-3-030-26072-9_28 | Wang Y., Liu Y., Zhang H., Xie H. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85069979622&doi=10.1007%2f978-3-030-26072-9_28&partnerID=40&md5=cd6cd31782a1a4a013236ca86cb45dfe | China | entity classification, relation classification, triple classification, knowledge graph embedding | validation research | technique | - |
| Conference Paper | Long Distance Entity Relation Extraction with Article Structure Embedding and Applied to Mining Medical Knowledge | Article structured embedding; Medical knowledge graph; Neural network; Relation extraction(...) | As a central work in medical knowledge graph construction, relation extraction has gained extensive attention in the fields of natural language processing and artificial intelligence. Conventional works on relation extraction share a common assumption: a sentence can express a relation of an entity pair only if both entities appear in this sentence. Under this assumption, plenty of informative sentences are precluded. In this paper, we break the assumption and propose a new relation extraction m(...) | IEEE | 2019 | 10.1109/ichi.2019.8904821 | Lin Y., Ma C., Gaoz D., Fan Z., Cheng Z., Wang Z., Yu S. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075919573&doi=10.1109%2fICHI.2019.8904821&partnerID=40&md5=2a1ff2bd2c943e17ef93413411419614 | China, United States | relation extraction | validation research | technique | health |
| Conference Paper | Long-Tail Relation Extraction Via Knowledge Graph Embeddings and Graph Convolution Networks | Computational linguistics; Convolution; Embeddings; Extraction; Knowledge management; Large dataset; Semantics; Attention mechanisms; Benchmark datasets; Class distributions; Coarse to fine; Imbalanced data; Knowledge graphs; Real world setting; Relation extraction; Data mining(...) | We propose a distance supervised relation extraction approach for long-tailed, imbalanced data which is prevalent in real-world settings. Here, the challenge is to learn accurate”few-shot” models for classes existing at the tail of the class distribution, for which little data is available. Inspired by the rich semantic correlations between classes at the long tail and those at the head, we take advantage of the knowledge from data-rich classes at the head of the distribution to boost the perfor(...) | ACL | 2019 | - | Zhang N., Deng S., Sun Z., Wang G., Chen X., Zhang W., Chen H. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085555333&partnerID=40&md5=c86d6bb110af1f2b70bac3b2301cc74f | China | relation extraction, knowledge graph embedding | validation research | technique | - |
| Conference Paper | Look before You Hop: Conversational Question Answering over Knowledge Graphs Using Judicious Context Expansion | Natural language processing systems; Different domains; Graph exploration; Interrogative sentences; Knowledge graphs; Question Answering; Question answering systems; State of the art; Unsupervised method; Knowledge management(...) | Fact-centric information needs are rarely one-shot; users typically ask follow-up questions to explore a topic. In such a conversational setting, the user's inputs are often incomplete, with entities or predicates left out, and ungrammatical phrases. This poses a huge challenge to question answering (QA) systems that typically rely on cues in full-fledged interrogative sentences. As a solution, we develop Convex: an unsupervised method that can answer incomplete questions over a knowledge graph (...) | ACM | 2019 | 10.1145/3357384.3358016 | Christmann P., Roy R.S., Abujabal A., Singh J., Weikum G. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075482520&doi=10.1145%2f3357384.3358016&partnerID=40&md5=5d66b42af52e47f26f93e8a4450a6180 | Germany | conversational interfaces, question answering | validation research | tool; resource | - |
| Conference Paper | Mining Scholarly Data for Fine-Grained Knowledge Graph Construction | Knowledge extraction; Knowledge graph; Natural language processing; Scholarly data; Semantic web(...) | Knowledge graphs (KG) are large networks of entities and relationships, typically expressed as RDF triples, relevant to a specific domain or an organization. Scientific Knowledge Graphs (SKGs) focus on the scholarly domain and typically contain metadata describing research publications such as authors, venues, organizations, research topics, and citations. The next big challenge in this field regards the generation of SKGs that also contain an explicit representation of the knowledge presented i(...) | Scopus | 2019 | - | Buscaldi D., Dessì D., Motta E., Osborne F., Recupero D.R. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067893695&partnerID=40&md5=967dee119fdc7b631fbde9a4e7380e9f | France, United Kingdom, Italy | entity extraction, relation extraction | solution proposal | method | scholarly domain |
| Conference Paper | Mining Scholarly Publications for Scientific Knowledge Graph Construction | Deep learning; Knowledge representation; Learning systems; Natural language processing systems; Text mining; Automatically generated; Knowledge extraction; Learning methods; NAtural language processing; Preliminary approach; Scholarly publication; Scientific knowledge; State of the art; Semantic Web(...) | In this paper, we present a preliminary approach that uses a set of NLP and Deep Learning methods for extracting entities and relationships from research publications and then integrates them in a Knowledge Graph. More specifically, we (i) tackle the challenge of knowledge extraction by employing several state-of-the-art Natural Language Processing and Text Mining tools, (ii) describe an approach for integrating entities and relationships generated by these tools, and (iii) analyse an automatica(...) | Scopus | 2019 | 10.1007/978-3-030-32327-1_2 | Buscaldi D., Dessì D., Motta E., Osborne F., Reforgiato Recupero D. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075566176&doi=10.1007%2f978-3-030-32327-1_2&partnerID=40&md5=145cac61036ad6ac137f15eeeba4d5cf | France, United Kingdom, Italy | entity extraction, relation extraction, entity linking | solution proposal | method | scholarly domain |
| Conference Paper | Modeling Multi-Mapping Relations for Precise Cross-Lingual Entity Alignment | - | Entity alignment aims to find entities in different knowledge graphs (KGs) that refer to the same real-world object. An effective solution for cross-lingual entity alignment is crucial for many cross-lingual AI and NLP applications. Recently many embedding-based approaches were proposed for cross-lingual entity alignment. However, almost all of them are based on TransE or its variants, which have been demonstrated by many studies to be unsuitable for encoding multi-mapping relations such as 1-N,(...) | ACL | 2019 | 10.18653/v1/d19-1075 | Shi, Xiaofei and Xiao, Yanghua | https://aclanthology.org/D19-1075 | China | knowledge graph embedding, entity alignment | validation research | technique | - |
| Conference Paper | Multi-Modal Question Answering System Driven by Domain Knowledge Graph | big data; domain knowledge graph; knowledge engineering; multimodal combination; question-answering system(...) | In the era of big data explosion, the Internet serving as an infrastructure for organizing and acquiring information and knowledge, has usability shortcomings in specific application scenarios. For professional application business, we need more efficient information organization and interactive interface to facilitate the formalization of expert experience and access to associated information by ordinary users. This paper designs a domain knowledge graph driven multi-modal question answering sy(...) | IEEE | 2019 | 10.1109/bigcom.2019.00015 | Zhao Z., Wang X., Xu X., Wang Q. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076108008&doi=10.1109%2fBIGCOM.2019.00015&partnerID=40&md5=35845999fecb21d86972e0156ad230bb | China | question answering | solution proposal | tool | health |
| Conference Paper | Named Entity Recognition in Traditional Chinese Medicine Clinical Cases Combining Bilstm-Crf with Knowledge Graph | Knowledge graph; Named entity recognition; Traditional Chinese Medicine(...) | Named entity recognition in Traditional Chinese Medicine (TCM) clinical cases is a fundamental and crucial task for follow-up work. In recent years, deep learning approaches have achieved remarkable results in named entity recognition and other natural language processing tasks. However, these methods cannot effectively solve the problem of low recognition rate of rare words, which is common in TCM field. In this paper, we propose TCMKG-LSTM-CRF model that utilizes knowledge graph information to(...) | Scopus | 2019 | 10.1007/978-3-030-29551-6_48 | Jin Z., Zhang Y., Kuang H., Yao L., Zhang W., Pan Y. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081618149&doi=10.1007%2f978-3-030-29551-6_48&partnerID=40&md5=bd7a0b9b206d53f25998ca9ffb7f9554 | China, United States | entity extraction, augmented language models | validation research | technique | health |
| Conference Paper | Natural Language Question/Answering with User Interaction over a Knowledge Base | Knowledge Graph; Natural Language Processing; User Interaction(...) | In the demo, we present RecipeFinder, a system for searching the information from knowledge graphs with natural language. The sys-tem has following characteristics: (1) It supports human-computer interaction, to resolve question ambiguity; (2) It provides graphical interface to help users refine questions. © 2019 Association for Computing Machinery.(...) | ACM | 2019 | 10.1145/3349341.3349425 | Zhan H., Sinha B., Jiang W. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073052577&doi=10.1145%2f3349341.3349425&partnerID=40&md5=33a83971729b436f7cd30afdce501eca | China | question answering | solution proposal | tool | food |
| Conference Paper | Node Embeddings for Graph Merging: Case of Knowledge Graph Construction | Embeddings; Errors; Graph algorithms; Graphic methods; Merging; Natural language processing systems; Error reduction; Graph-based; Knowledge graphs; Matching methods; Process errors; String similarity; Text corpora; Two-graphs; Graph theory(...) | Combining two graphs requires merging the nodes which are counterparts of each other. In this process errors occur, resulting in incorrect merging or incorrect failure to merge. We find a high prevalence of such errors when using AskNET, an algorithm for building Knowledge Graphs from text corpora. AskNET node matching method uses string similarity, which we propose to replace with vector embedding similarity. We explore graph-based and wordbased embedding models and show an overall error reduct(...) | ACL | 2019 | - | Szubert I., Steedman M. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085036865&partnerID=40&md5=28bd67adc5310fa8700c9bfc54d763b0 | United Kingdom | entity alignment, knowledge graph embedding | validation research | technique | - |
| Conference Paper | Okgraph: Unsupervised Structured Data Extraction from Plain Text | Knowledge graphs; Machine understanding; Unsupervised learning; Word embeddings(...) | In this report we introduce OKgraph, a software library for (open) Knowledge Graph extraction from free text. Named after a two-year project where we studied and developed unsupervised algorithms addressing tasks related to taxonomy learning, the library contains NLP tools powered by these results. Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).(...) | Scopus | 2019 | - | Atzori M., Balloccu S., Bellanti A., Mameli E., Usai S.R. | http://ceur-ws.org/Vol-2441/paper19.pdf | Italy | entity classification, entity extraction, relation extraction | validation research | tool | - |
| Conference Paper | Old Is Gold: Linguistic Driven Approach for Entity and Relation Linking of Short Text | Computational linguistics; Gold; Back-ground knowledge; Empirical studies; Fundamental principles; Knowledge graphs; Knowledge sources; Linguistic approach; Named entity recognition; State of the art; Knowledge management(...) | Short texts challenge NLP tasks such as named entity recognition, disambiguation, linking and relation inference because they do not provide sufficient context or are partially malformed (e.g. wrt. capitalization, long tail entities, implicit relations). In this work, we present the Falcon approach which effectively maps entities and relations within a short text to its mentions of a background knowledge graph. Falcon overcomes the challenges of short text using a light-weight linguistic approac(...) | ACL | 2019 | - | Sakor A., Mulang I.O., Singh K., Shekarpour S., Vidal M.-E., Lehmann J., Auer S. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081090168&partnerID=40&md5=d93ebebd95c7e03c292d4b885c224512 | Germany, United States | entity linking, relation linking | validation research | tool | - |
| Conference Paper | Pingan Smart Health and Sjtu at Coin - Shared Task: Utilizing Pre-Trained Language Models and Common-Sense Knowledge in Machine Reading Tasks | - | To solve the shared tasks of COIN: COmmonsense INference in Natural Language Processing) Workshop in , we need explore the impact of knowledge representation in modeling commonsense knowledge to boost performance of machine reading comprehension beyond simple text matching. There are two approaches to represent knowledge in the low-dimensional space. The first is to leverage large-scale unsupervised text corpus to train fixed or contextual language representations. The second approach is to expl(...) | ACL | 2019 | 10.18653/v1/d19-6011 | Li, Xiepeng and Zhang, Zhexi and Zhu, Wei and Li, Zheng and Ni, Yuan and Gao, Peng and Yan, Junchi and Xie, Guotong | https://aclanthology.org/D19-6011 | China | augmented language models, question answering | validation research | technique | - |
| Conference Paper | Playing Text-Adventure Games with Graph-Based Deep Reinforcement Learning | Computational linguistics; Graphic methods; Natural language processing systems; Reinforcement learning; Transfer learning; Action spaces; Adventure games; Combinatorial action; Control policy; Graph-based; Knowledge graphs; Natural languages; Question Answering Task; Deep learning(...) | Text-based adventure games provide a platform on which to explore reinforcement learning in the context of a combinatorial action space, such as natural language. We present a deep reinforcement learning architecture that represents the game state as a knowledge graph which is learned during exploration. This graph is used to prune the action space, enabling more efficient exploration. The question of which action to take can be reduced to a question-answering task, a form of transfer learning t(...) | ACL | 2019 | - | Ammanabrolu P., Riedl M.O. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084309358&partnerID=40&md5=d963a79c4d1dc698fbf1568ac13464d2 | Georgia, United States | question answering | validation research | tool | entertainment media |
| Conference Paper | Qaldgen: Towards Microbenchmarking of Question Answering Systems over Knowledge Graphs | Benchmarking; Clustering algorithms; HTTP; Natural language processing systems; Open source software; GNU general public license; Important features; Knowledge graphs; Micro-benchmarking; Natural language questions; Question Answering; Question answering systems; State of the art; Semantic Web(...) | Over the last years, a number of Knowledge Graph (KG) based Question Answering (QA) systems have been developed. Consequently, the series of Question Answering Over Linked Data (QALD1–QALD9) challenges and other datasets have been proposed to evaluate these systems. However, the QA datasets contain a fixed number of natural language questions and do not allow users to select micro benchmarking samples of the questions tailored towards specific use-cases. We propose QaldGen, a framework for micro(...) | Scopus | 2019 | 10.1007/978-3-030-30796-7_18 | Singh K., Saleem M., Nadgeri A., Conrads F., Pan J.Z., Ngomo A.-C.N., Lehmann J. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081092804&doi=10.1007%2f978-3-030-30796-7_18&partnerID=40&md5=bdf48e5071cdd83a30a784c944b85152 | Germany, United Kingdom, India | question answering | validation research | tool; resource | - |
| Journal Article | Qanalysis: a Question-Answer Driven Analytic Tool on Knowledge Graphs for Leveraging Electronic Medical Records for Clinical Research | Electronic medical record; Statistical question answering; Graph database; Context-free grammar(...) | BackgroundWhile doctors should analyze a large amount of electronic medical record (EMR) data to conduct clinical research, the analyzing process requires information technology (IT) skills, which is difficult for most doctors in China.MethodsIn this paper, we build a novel tool QAnalysis, where doctors enter their analytic requirements in their natural language and then the tool returns charts and tables to the doctors. For a given question from a user, we first segment the sentence, and then w(...) | WoS | 2019 | 10.1186/s12911-019-0798-8 | Ruan T,Huang Y,Liu X,Xia Y,Gao J | http://dx.doi.org/10.1186/s12911-019-0798-8 | China | question answering | solution proposal | tool | health |
| Conference Paper | Querying Knowledge Graphs with Natural Languages | Expert systems; Graphic methods; Natural language processing systems; Pattern matching; Semantics; Graph pattern matching; Knowledge graphs; Natural language queries; Natural languages; Query algorithms; Query evaluation; Subgraph isomorphism; Top-k-matches; Query processing(...) | With the unprecedented proliferation of knowledge graphs, how to process query evaluation over them becomes increasingly important. On knowledge graphs, queries are typically evaluated with graph pattern matching, i.e., given a pattern query Q and a knowledge graph G, it is to find the set M(Q, G) of matches of Q in G, where matching is defined with subgraph isomorphism. However querying big knowledge graphs brings us challenges: (1) queries are often issued with natural languages, hence can not(...) | Scopus | 2019 | 10.1007/978-3-030-27618-8_3 | Wang X., Yang L., Zhu Y., Zhan H., Jin Y. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077129551&doi=10.1007%2f978-3-030-27618-8_3&partnerID=40&md5=cd4fd8ce028f66e5f94e3a0eed394057 | China | question answering | validation research | tool | - |
| Conference Paper | Question-Answering System Based on the Knowledge Graph of Traditional Chinese Medicine | Chinese medicine; Knowledge Graph; Natural language processing; Question analysis; Question-Answering system(...) | With the development of artificial intelligence, the emergence of the QA system meets the search needs of people in the mass information age. The traditional question-Answering system mostly matches the questions with fixed templates, and the dataset of questions and answers often rely on human-designed features, which is time-consuming and with low accuracy. To address this dilemma, the current prevailing technology of Knowledge Graph provides a new way, helping to build a domain-specific intel(...) | IEEE | 2019 | 10.1109/ihmsc.2019.10156 | Miao F., Wang X., Zhang P., Jin L. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078299443&doi=10.1109%2fIHMSC.2019.10156&partnerID=40&md5=024366ba65e4442e6b9d76545a6577e6 | China | question answering | solution proposal | tool | health |
| Conference Paper | Reasoning over Paths Via Knowledge Base Completion | Graphic methods; Knowledge based systems; Natural language processing systems; High frequency HF; Knowledge base; Knowledge graphs; Scientific literature; Simple approach; Vector representations; Graph theory(...) | Reasoning over paths in large scale knowledge graphs is an important problem for many applications. In this paper we discuss a simple approach to automatically build and rank paths between a source and target entity pair with learned embeddings using a knowledge base completion model (KBC). We assembled a knowledge graph by mining the available biomedical scientific literature and extracted a set of high frequency paths to use for validation. We demonstrate that our method is able to effectively(...) | ACL | 2019 | - | Sudhahar S., Roberts I., Pierleoni A. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085036780&partnerID=40&md5=f5630074988db318a2f4d5f30aabbf28 | United Kingdom | relation classification, knowledge graph embedding, entity extraction, relation extraction | validation research | technique | health |
| Conference Paper | Relation Classification in Knowledge Graph Based on Natural Language Text | bidirectinal GRU; distant supervisin; knowledge graph; relation classification(...) | Relation classification is an important semantic processing task in natural language processing, and it is also an important task to construct knowledge graph based on natural language text. At present, the cutting-edge method in the field of natural language processing is to obtain some advanced features based on deep learning. One problem is that important features of a sentence can appear anywhere in the sentence. Another problem is that building a domain-specific knowledge map often lacks an(...) | IEEE | 2019 | 10.1109/icsess.2018.8663945 | Song Y., Rao R.N., Shi J. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063649445&doi=10.1109%2fICSESS.2018.8663945&partnerID=40&md5=f9c3bdb4ce94a8ff6122266299726025 | China | relation classification | validation research | technique | - |
| Conference Paper | Relation Extraction of Chinese Fundamentals of Electric Circuits Textbook Based on Cnn | Chinese fundamentals of electric circuits; Convolutional neural network; Relation extraction(...) | Deep neural network has been widely used in a variety of natural language processing (NLP) tasks nowadays. As one of the most import research areas, entity relation extraction applies usual recurrent neural networks (RNNs) and convolutional neural networks (CNNs) and has achieved good results. Most relation extraction tasks are about public and general datasets, they are usually natural languages or daily conversations, and have millions of samples, very few relates to small corpus in a specific(...) | IEEE | 2019 | 10.1109/itnec.2019.8729144 | Li Y., Chen X., Bao Y., Guo D., Huang X. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067892226&doi=10.1109%2fITNEC.2019.8729144&partnerID=40&md5=73dd4cf34e78a979c2a19cc7decce786 | China | relation extraction | solution proposal | technique | engineering |
| Conference Paper | Relation Prediction for Unseen-Entities Using Entity-Word Graphs | Forecasting; Graph structures; Graphic methods; Knowledge graphs; Word graphs; Natural language processing systems(...) | Knowledge graphs (KGs) are generally used for various NLP tasks. However, as KGs still miss some information, it is necessary to develop Knowledge Graph Completion (KGC) methods. Most KGC researches do not focus on the Out-of-KGs entities (Unseen-entities), we need a method that can predict the relation for the entity pairs containing Unseen-entities to automatically add new entities to the KGs. In this study, we focus on relation prediction and propose a method to learn entity representations v(...) | ACL | 2019 | - | Tagawa Y., Taniguchi M., Miura Y., Taniguchi T., Ohkuma T., Yamamoto T., Nemoto K. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085025805&partnerID=40&md5=5335d10699cca3d052813bbbbd3ed7e4 | Japan | knowledge graph embedding, relation classification | validation research | technique | - |
| Conference Paper | Robotic Task Oriented Knowledge Graph for Human-Robot Collaboration in Disassembly | Human-robot collaboration; Knowledge base; Knowledge graph; Product disassembly(...) | Traditional disassembly methods, such as manual and robotic disassembly, are no longer competent for the requirement of the complexity of the disassembly product. Therefore, the human-robot collaboration concept can be introduced to realize a novel disassembly system, towards increasing the flexibility and adaptability of them. In order to facilitate the efficient and smooth human-robot collaboration in disassembly, it is necessary to make the disassembly system more intelligent. In this paper, (...) | ScienceDirect | 2019 | 10.1016/j.procir.2019.03.121 | Ding Y., Xu W., Liu Z., Zhou Z., Pham D.T. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85070543962&doi=10.1016%2fj.procir.2019.03.121&partnerID=40&md5=2a1848df6af97ba412de051e321de965 | China, United Kingdom | entity extraction, relation extraction | solution proposal | tool | engineering |
| Conference Paper | Scalable Knowledge Graph Construction over Text Using Deep Learning Based Predicate Mapping | Deep Learning; Knowledge Graph; Predicate Mapping; Scalability; Sentence Simplification(...) | Automatic extraction of information from text and its transformation into a structured format is an important goal in both Semantic Web Research and computational linguistics. Knowledge Graphs (KG) serve as an intuitive way to provide structure to unstructured text. A fact in a KG is expressed in the form of a triple which captures entities and their interrelationships (predicates). Multiple triples extracted from text can be semantically identical but they may have a vocabulary gap which could (...) | Scopus | 2019 | 10.1145/3308560.3317708 | Mehta A., Singhal A., Karlapalem K. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85066883504&doi=10.1145%2f3308560.3317708&partnerID=40&md5=a9f7161e47f9f88cb0bf4f6c7fbbf75e | India | entity extraction, relation extraction, entity linking, relation linking | validation research | method | - |
| Journal Article | Scalable Micro-Planned Generation of Discourse from Structured Data | - | We present a framework for generating natural language description from structured data such as tables; the problem comes under the category of data-to-text natural language generation (NLG). Modern data-to-text NLG systems typically use end-to-end statistical and neural architectures that learn from a limited amount of task-specific labeled data, and therefore exhibit limited scalability, domain-adaptability, and interpretability. Unlike these systems, ours is a modular, pipeline-based approach(...) | ACL | 2019 | 10.1162/coli_a_00363 | Laha, Anirban and Jain, Parag and Mishra, Abhijit and Sankaranarayanan, Karthik | https://aclanthology.org/J19-4005 | Canada, India, United Kingdom | text generation | validation research | tool; resource | - |
| Conference Paper | Scalable, Semi-Supervised Extraction of Structured Information from Scientific Literature | - | As scientific communities grow and evolve, there is a high demand for improved methods for finding relevant papers, comparing papers on similar topics and studying trends in the research community. All these tasks involve the common problem of extracting structured information from scientific articles. In this paper, we propose a novel, scalable, semi-supervised method for extracting relevant structured information from the vast available raw scientific literature. We extract the fundamental con(...) | ACL | 2019 | 10.18653/v1/w19-2602 | Agrawal, Kritika and Mittal, Aakash and Pudi, Vikram | https://aclanthology.org/W19-2602 | India | entity extraction, relation extraction, semantic search | validation research | method | scholarly domain |
| Conference Paper | Semantic Data Integration Techniques for Transforming Big Biomedical Data into Actionable Knowledge | Big Data; Biomedical Data; Knowledge Graph; Natural Language Processing; Semantic Data Integration(...) | FAIR principles and the Open Data initiatives have motivated the publication of large volumes of data. Specifically, in the biomedical domain, the size of the data has increased exponentially in the last decade, and with the advances in the technologies to collect and generate data, a faster growth rate is expected for the next years. The available collections of data are characterized by the dominant dimensions of big data, i.e., they are not only large in volume, but they can be also heterogen(...) | IEEE | 2019 | 10.1109/cbms.2019.00116 | Vidal M.-E., Jozashoori S. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85070971867&doi=10.1109%2fCBMS.2019.00116&partnerID=40&md5=05886717e5fc8034a9d0bc85bc45b07e | Germany | entity extraction, relation extraction, semantic search | solution proposal | method | health |
| Conference Paper | Semantic Similarity Computation in Knowledge Graphs: Comparisons and Improvements | Knowledge graph; Semantic similarity; Synonym(...) | Computing semantic similarity between concepts is a fundamental task in natural language processing and has a large variety of applications. In this paper, first of all, we will review and analyze existing semantic similarity computation methods in knowledge graphs. Through the analysis of these methods, we find that existing works mainly focus on the context features of concepts which indicate the position or the frequency of the concepts in the knowledge graphs, such as the depth of terms, inf(...) | Scopus | 2019 | 10.1109/icdew.2019.000-5 | Yang C., Zhu Y., Zhong M., Li R. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85069231114&doi=10.1109%2fICDEW.2019.000-5&partnerID=40&md5=9c72f6589f2083035fe8398c243a18fc | China | semantic similarity | validation research | technique | - |
| Conference Paper | Simple Question Answering with Subgraph Ranking and Joint-Scoring | Graphic methods; Knowledge based systems; Knowledge base; Knowledge graphs; Loss functions; Question Answering; Ranking methods; Research communities; State of the art; Unified framework; Computational linguistics(...) | Knowledge graph based simple question answering (KBSQA) is a major area of research within question answering. Although only dealing with simple questions, i.e., questions that can be answered through a single knowledge base (KB) fact, this task is neither simple nor close to being solved. Targeting on the two main steps, subgraph selection and fact selection, the research community has developed sophisticated approaches. However, the importance of subgraph ranking and leveraging the subject-rel(...) | ACL | 2019 | - | Zhao W., Chung T., Goyal A., Metallinou A. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085585112&partnerID=40&md5=ca516900d7646ea683baa65ab240ddb9 | United States | question answering | validation research | technique | - |
| Conference Paper | Smartkt: a Search Framework to Assist Program Comprehension Using Smart Knowledge Transfer | Knowledge Graph; Knowledge Transfer; Machine Learning; Natural Language Processing; Program Comprehension(...) | Regardless of attempts to extract knowledge from code bases to aid in program comprehension, there is an absence of a framework to extract and integrate knowledge to provide a near-complete multifaceted understanding of a program. To bridge this gap, we propose SMARTKT (Smart Knowledge Transfer) to extract and transfer knowledge related to software development and application-specific characteristics and their interrelationships in form of a knowledge graph. For an application, the knowledge gra(...) | IEEE | 2019 | 10.1109/qrs.2019.00026 | Majumdar S., Papdeja S., Das P.P., Ghosh S.K. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073781599&doi=10.1109%2fQRS.2019.00026&partnerID=40&md5=697f3e53a7776f335a18730f364fa238 | India | semantic search | solution proposal | method | engineering |
| Conference Paper | Study on Framework of Intelligent Analysis of Chinese Preview Homework in Primary Schools | Artificial Intelligence; Personalized Teaching; Semantic Analysis; Text Recognition(...) | Aiming at relieving heavy workloads of primary school Chinese teachers to revise students' preview homework every day, an AI-based intelligent analysis framework is put forward to revise, analyze and generate statistic and individual reports for teachers to carry out personalized teaching and assign personalized homework to each student. After combing related technologies such as optical character recognition, natural language processing, semantic analysis and knowledge graph, feasibility of thi(...) | IEEE | 2019 | 10.1109/cac48633.2019.8996356 | Gong X., Liu X., Jing S., Li Q., Zhang N., Luo J., Yan Y., Lu H., Guan Y. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85080101471&doi=10.1109%2fCAC48633.2019.8996356&partnerID=40&md5=977747c941582385bf81c23b82142d5b | China | semantic search | solution proposal | method | education |
| Conference Paper | Technology Knowledge Graph for Design Exploration: Application to Designing the Future of Flying Cars | Knowledge graph; Natural language processing; Patent analysis; Patent analysis; Semantic-level knowledge(...) | To pursue innovation, design engineers need to continuously exploit the knowledge in their design domain and explore other relevant knowledge around the domain. While many methods and tools have been developed to retrieve knowledge within a given design domain, e.g., flying cars, knowledge discovery beyond the domain for innovation remains a challenge, and relevant methods are under-developed. Herein, we introduce a methodology to use a technology knowledge graph (TKG), which covers sematic-leve(...) | Scopus | 2019 | 10.1115/detc2019-97605 | Sarica S., Song B., Luo J., Wood K. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076406319&doi=10.1115%2fDETC2019-97605&partnerID=40&md5=f5bd3c7bb4e1b635854ead0f0e26c5d9 | Singapore | entity extraction, relation extraction, semantic search | solution proposal | method | engineering |
| Conference Paper | Text Generation from Knowledge Graphs with Graph Transformers | Computational linguistics; Decoding; Knowledge management; Knowledge representation; Signal encoding; Ubiquitous computing; Document structure; Human evaluation; Information extraction systems; Knowledge graphs; Long-distance dependencies; Relational structures; Scientific texts; Trainable system; Graph structures(...) | Generating texts which express complex ideas spanning multiple sentences requires a structured representation of their content (document plan), but these representations are prohibitively expensive to manually produce. In this work, we address the problem of generating coherent multi-sentence texts from the output of an information extraction system, and in particular a knowledge graph. Graphical knowledge representations are ubiquitous in computing, but pose a significant challenge for text gen(...) | ACL | 2019 | - | Koncel-Kedziorski R., Bekal D., Luan Y., Lapata M., Hajishirzi H. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079655513&partnerID=40&md5=ca349cd558be9df6d472894bc5d0745f | United Kingdom, United States | augmented language models, data-to-text generation | validation research | tool | scholarly domain |
| Conference Paper | The Magic of Semantic Enrichment and Nlp for Medical Coding | Knowledge graphs; Medical coding; Natural Language Processing (NLP); Semantic enrichment; Word embeddings(...) | Artificial Intelligence technologies are every day more present in the medical domain. Several healthcare activities that were entirely done manually by experts in the past, now are reaching a high level of automatization thanks to a satisfactory integration between these technologies and the medical professionals. This is the case of the medical coding process, consisting on the annotation of clinical notes (free-text narrative reports) to standard medical classifications in order to align this(...) | Scopus | 2019 | 10.1007/978-3-030-32327-1_12 | García-Santa N., San Miguel B., Ugai T. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075575311&doi=10.1007%2f978-3-030-32327-1_12&partnerID=40&md5=30bde39ddf657142ebf5206687ddda25 | Spain, Japan | entity extraction, relation extraction | solution proposal | tool | health |
| Conference Paper | Transfer in Deep Reinforcement Learning Using Knowledge Graphs | Computer games; Graphic methods; Intelligent agents; Knowledge management; Learning systems; Natural language processing systems; Quality control; Reinforcement learning; Transfer learning; Adventure games; Domain knowledge; Knowledge graphs; Multiple computers; Question Answering; Reinforcement learning agent; State representation; Transfer learning methods; Deep learning(...) | Text adventure games, in which players must make sense of the world through text descriptions and declare actions through text descriptions, provide a stepping stone toward grounding action in language. Prior work has demonstrated that using a knowledge graph as a state representation and question-answering to pre-train a deep Q-network facilitates faster control policy learning. In this paper, we explore the use of knowledge graphs as a representation for domain knowledge transfer for training (...) | ACL | 2019 | - | Ammanabrolu P., Riedl M.O. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085045262&partnerID=40&md5=c7c708325d80bd2488a7a6e8129a96f0 | Georgia, United States | question answering | validation research | method | entertainment media |
| Conference Paper | Uhop: an Unrestricted-Hop Relation Extraction Framework for Knowledge-Based Question Answering | Computational linguistics; Extraction; Knowledge based systems; Competitive performance; Knowledge based; Knowledge graphs; Number of hops; Performance gaps; Question Answering; Relation extraction; State of the art; Data mining(...) | In relation extraction for knowledge-based question answering, searching from one entity to another entity via a single relation is called “one hop”. In related work, an exhaustive search from all one-hop relations, two-hop relations, and so on to the max-hop relations in the knowledge graph is necessary but expensive. Therefore, the number of hops is generally restricted to two or three. In this paper, we propose UHop, an unrestricted-hop framework which relaxes this restriction by use of a tra(...) | ACL | 2019 | - | Chen Z.-Y., Chang C.-H., Chen Y.-P., Nayak J., Ku L.-W. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078931467&partnerID=40&md5=848b379bc05192f761eae9408b6baf8d | India, United States | question answering, relation extraction | validation research | technique | - |
| Conference Paper | Webisagraph: a Very Large Hypernymy Graph from a Web Corpus | Computational linguistics; Knowledge graphs; Large graphs; Plug-ins; Web Corpora; Web texts; Large dataset(...) | In this paper, we present WebIsAGraph, a very large hypernymy graph compiled from a dataset of is-a relationships extracted from the CommonCrawl. We provide the resource together with a Neo4j plugin to enable efficient searching and querying over such large graph. We use WebIsAGraph to study the problem of detecting polysemous terms in a noisy terminological knowledge graph, thus quantifying the degree of polysemy of terms found in is-a extractions from Web text. Copyright © 2019 for this paper (...) | Scopus | 2019 | - | Faralli S., Finocchi I., Ponzetto S.P., Velardi P. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074808871&partnerID=40&md5=eca0a652e88be374a3995f78c37590b0 | Germany, Italy | entity extraction, relation extraction | validation research | resource | - |
| Conference Paper | A Deep Learning Knowledge Graph Approach to Drug Labelling | deep learning; drug labels; knowledge graph embeddings; LSTM(...) | Ensuring the accuracy and completeness of drug labels is a labour-intensive and potentially error prone process, as labels contain unstructured text that is not suitable for automated processing. To address this, we have developed a novel deep learning system that uses a bidirectional LSTM model to extract and structure drug information in a knowledge graph-based embedding space. This allows us to evaluate drug label consistency with ground truth knowledge, along with the ability to predict addi(...) | IEEE | 2020 | 10.1109/bibm49941.2020.9313350 | Sastre J., Zaman F., Duggan N., McDonagh C., Walsh P. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100330601&doi=10.1109%2fBIBM49941.2020.9313350&partnerID=40&md5=833d1e46def4c00273ca698715e038c3 | Ireland | knowledge graph embedding, entity extraction | validation research | technique | health |
| Conference Paper | A Framework for Modeling Knowledge Graphs Via Processing Natural Descriptions of Vehicle-Pedestrian Interactions | Knowledge graph; Natural language processing; Pedestrian behavior(...) | The full-scale deployment of autonomous driving demands successful interaction with pedestrians and other vulnerable road users, which requires an understanding of their dynamic behavior and intention. Current research achieves this by estimating pedestrian’s trajectory mainly based on the gait and movement information in the past as well as other relevant scene information. However, the autonomous vehicles still struggle with such interactions since the visual features alone may not supply subt(...) | Scopus | 2020 | 10.1007/978-3-030-59987-4_4 | Elahi M.F., Luo X., Tian R. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097241483&doi=10.1007%2f978-3-030-59987-4_4&partnerID=40&md5=2c9010bbacc169f5efb4b0475662670d | India, United States | entity extraction, relation extraction | solution proposal | method | engineering |
| Conference Paper | A Framework for a Comprehensive Conceptualization of Urban Constructs | Case-based reasoning (cbr) and case-based design (cbd); Deep neural network for structuring kg; Domain-specific knowledge graph of urban qualities; Natural language processing and comprehensive understanding of urban constructs; Urban cognition and design creativity(...) | Analogy is thought to be foundational for designing and for design creativity. Nonetheless, practicing analogical reasoning needs a knowledge-base. The paper proposes a framework for constructing a knowledge-base of urban constructs that builds on an ontology of urbanism. The framework is composed of two modules that are responsible for representing either the concepts or the features of any urban constructs' materialization. The concepts are represented as a knowledge graph (KG) named SpatialNe(...) | Scopus | 2020 | - | Ezzat M. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091286980&partnerID=40&md5=6481b239274ce5a522c91a20a662588e | Egypt | natural language inference, ontology construction | solution proposal | method | engineering |
| Conference Paper | A Knowledge Graph Embedding Method Based on Neural Network | Knowledge graph; Knowledge graph embedding; Link prediction; Neural network(...) | As the basis of many knowledge graph completion tasks, the embedding representation of entities and relations in knowledge graph (KG) is an important task in the fields of Natural Language Processing (NLP) and Artificial Intelligence (AI). While most of the existing knowledge graph embedding (KGE) models based on convolutional neural network (CNN) can obtain abundant feature embedding, they may ignore an important fact that the triples in the KG come from the text, as they simply learn about the(...) | IEEE | 2020 | 10.1109/dsc50466.2020.00057 | Li C., Li A., Tu H., Wang Y., Wang C. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092048856&doi=10.1109%2fDSC50466.2020.00057&partnerID=40&md5=545749b4ee55b53f50734d4c1d62ab15 | China | knowledge graph embedding, link prediction | validation research | technique | - |
| Journal Article | A Knowledge Graph-Aided Concept-Knowledge Approach for Evolutionary Smart Product-Service System Development | Concept generation; Conceptual design; Concept–knowledge model; Creativity; Knowledge evolution; Knowledge graph; Smart product–service system(...) | In order to meet user expectations and to optimize user experience with a higher degree of flexibility and sustainability, the Smart product–service system (Smart PSS), as a novel value proposition paradigm considering both online and offline smartness, was proposed. However, conventional manners for developing PSS require many professional consultations and still cannot meet with the new features of Smart PSS, such as user context-awareness and ever-evolving knowledge management. Therefore, aim(...) | Scopus | 2020 | 10.1115/1.4046807 | Li X., Chen C.-H., Zheng P., Wang Z., Jiang Z., Jiang Z. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089729213&doi=10.1115%2f1.4046807&partnerID=40&md5=abaec16b11d639f166214b6b1d1beff3 | China, Hong Kong, Singapore | entity extraction, relation extraction, ontology construction, semantic search | solution proposal | method; guidelines | business |
| Conference Paper | A Knowledge-Aware Sequence-To-Tree Network for Math Word Problem Solving | - | With the advancements in natural language processing tasks, math word problem solving has received increasing attention. Previous methods have achieved promising results but ignore background common-sense knowledge not directly provided by the problem. In addition, during generation, they focus on local features while neglecting global information. To incorporate external knowledge and global expression information, we propose a novel knowledge-aware sequence-to-tree (KA-S2T) network in which th(...) | ACL | 2020 | 10.18653/v1/2020.emnlp-main.579 | Wu, Qinzhuo and Zhang, Qi and Fu, Jinlan and Huang, Xuanjing | https://aclanthology.org/2020.emnlp-main.579 | China | knowledge graph embedding, natural language inference | validation research | technique | - |
| Journal Article | A Knowledge-Graph Platform for Newsrooms | Computational journalism, Journalistic knowledge platforms, Newsroom systems, Knowledge graphs, Semantic technologies, RDF, OWL, Ontology, Natural-language processing (NLP), Machine learning (ML)(...) | Journalism is challenged by digitalisation and social media, resulting in lower subscription numbers and reduced advertising income. Information and communication techniques (ICT) offer new opportunities. Our research group is collaborating with a software developer of news production tools for the international market to explore how social, open, and other data sources can be leveraged for journalistic purposes. We have developed an architecture and prototype called News Hunter that uses knowle(...) | ScienceDirect | 2020 | 10.1016/j.compind.2020.103321 | Arne Berven and Ole A. Christensen and Sindre Moldeklev and Andreas L. Opdahl and Kjetil J. Villanger | https://www.sciencedirect.com/science/article/pii/S0166361520305558 | Norway | semantic search | solution proposal | tool | news |
| Conference Paper | A Non-Commutative Bilinear Model for Answering Path Queries in Knowledge Graphs | Computational efficiency; Knowledge representation; Block-circulant matrices; Circulant matrix; Diagonal matrices; Fast computation; Knowledge graphs; Matrix products; Non-commutative; Relation matrix; Natural language processing systems(...) | Bilinear diagonal models for knowledge graph embedding (KGE), such as DistMult and ComplEx, balance expressiveness and computational efficiency by representing relations as diagonal matrices. Although they perform well in predicting atomic relations, composite relations (relation paths) cannot be modeled naturally by the product of relation matrices, as the product of diagonal matrices is commutative and hence invariant with the order of relations. In this paper, we propose a new bilinear KGE mo(...) | ACL | 2020 | - | Hayashi K., Shimbo M. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084313620&partnerID=40&md5=bb6763f99dc0dcc882f6eb185a9a50fb | Japan | knowledge graph embedding, question answering | validation research | technique | - |
| Conference Paper | A Question Answering System of Ethnic Minorities Based on Knowledge Graph | Knowledge graph; Named entity recognition; Question answering system; Question classification; Similarity calculation(...) | In recent years, Question Answering System has become a main focus of human machine interaction. Using the question answering system for information retrieval is convenient and efficient. Traditional question answering systems mostly use template matching. The question and answer data sets usually rely on manual design. The question and answer system implemented by this method has a quick query response and can answer relatively complex questions. But manually defining templates and rules is tim(...) | IEEE | 2020 | 10.1109/pic50277.2020.9350829 | Li J., Liu S., Yang H., Kolmanic S. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101675772&doi=10.1109%2fPIC50277.2020.9350829&partnerID=40&md5=1990665c6785dfc49ea6c55a0fa8e93b | China, Slovenia | question answering | validation research | tool | culture |
| Conference Paper | A Re-Evaluation of Knowledge Graph Completion Methods | - | Knowledge Graph Completion (KGC) aims at automatically predicting missing links for large-scale knowledge graphs. A vast number of state-of-the-art KGC techniques have got published at top conferences in several research fields, including data mining, machine learning, and natural language processing. However, we notice that several recent papers report very high performance, which largely outperforms previous state-of-the-art methods. In this paper, we find that this can be attributed to the in(...) | ACL | 2020 | 10.18653/v1/2020.acl-main.489 | Sun, Zhiqing and Vashishth, Shikhar and Sanyal, Soumya and Talukdar, Partha and Yang, Yiming | https://aclanthology.org/2020.acl-main.489 | India, United States | triple classification, knowledge graph embedding | validation research | method | - |
| Conference Paper | A Review of Knowledge Graph Technology in the Field of Automatic Question Answering | automatic question answering system; knowledge graph; knowledge system; natural language processing(...) | The automatic question answering (QA) system is a typical natural language processing task. How to make the automatic question answering system more intelligent is a popular research direction in the field of natural language processing. In this era of information explosion, the multisource of data itself makes it difficult to integrate and manage. To solve such problems, it is particularly important to construct and present a complete knowledge system. The knowledge graph (KG) shows real-world (...) | IEEE | 2020 | 10.1109/ispcem52197.2020.00042 | Zhang F., Zhang Y., Xu T. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114208400&doi=10.1109%2fISPCEM52197.2020.00042&partnerID=40&md5=e400a205a6e5615e7fd8af8e585a9864 | China | question answering | secondary research | method; guidelines | - |
| Journal Article | A Review: Knowledge Reasoning over Knowledge Graph | Knowledge graph, Reasoning, Rule-based reasoning, Distributed representation-based reasoning, Neural network-based reasoning(...) | Mining valuable hidden knowledge from large-scale data relies on the support of reasoning technology. Knowledge graphs, as a new type of knowledge representation, have gained much attention in natural language processing. Knowledge graphs can effectively organize and represent knowledge so that it can be efficiently utilized in advanced applications. Recently, reasoning over knowledge graphs has become a hot research topic, since it can obtain new knowledge and conclusions from existing data. He(...) | ScienceDirect | 2020 | 10.1016/j.eswa.2019.112948 | Xiaojun Chen and Shengbin Jia and Yang Xiang | https://www.sciencedirect.com/science/article/pii/S0957417419306669 | China | knowledge graph embedding, question answering, link prediction, entity classification | secondary research | guidelines | - |
| Conference Paper | A Sentiment-Controllable Topic-To-Essay Generator with Topic Knowledge Graph | Computational linguistics; Decoding; Natural language processing systems; Semantics; Auto encoders; Automatic evaluation; Human evaluation; Knowledge graphs; Natural language generation; Semantics Information; State-of-the-art approach; Topic diversity; Topic relevance; Topic words; Knowledge graph(...) | Generating a vivid, novel, and diverse essay with only several given topic words is a challenging task of natural language generation. In previous work, there are two problems left unsolved: neglect of sentiment beneath the text and insufficient utilization of topic-related knowledge. Therefore, we propose a novel Sentiment-Controllable topic-to-essay generator with a Topic Knowledge Graph enhanced decoder, named SCTKG, which is based on the conditional variational autoencoder (CVAE) framework. (...) | ACL | 2020 | - | Qiao L., Yan J., Meng F., Yang Z., Zhou J. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108643415&partnerID=40&md5=4bba9f75387d4fc4e1315fe19d27810e | China | text generation, augmented language models | validation research | technique | - |
| Conference Paper | A Spatiotemporal Knowledge Bank from Rape News Articles for Decision Support | Knowledge graph; Location-based; Ontology; PeNLP Parser; Spatiotemporal(...) | Rape cases have been on the increase during the COVID’19 pandemic. All News media including the online Newsfeed report these cases around our communities. It is important for intending visitors or residents to be properly informed of specific locations and the times these occurrences are predominant. Our proposed model is aimed at providing a spatiotemporal knowledge bank useful for personal, governmental and/or organizational decision support on occurrences like rape and armed robbery. This mod(...) | Scopus | 2020 | 10.1007/978-3-030-65384-2_11 | Usip P.U., Ijebu F.F., Dan E.A. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098253496&doi=10.1007%2f978-3-030-65384-2_11&partnerID=40&md5=a7473ee4b0c5873dbd5b6edbaa4928db | Niger, Nigeria | entity extraction, relation extraction, ontology construction, semantic search | solution proposal | method | law |
| Conference Paper | A Spreading Activation Framework for Tracking Conceptual Complexity of Texts | Computational linguistics; Semantics; Dbpedia; Knowledge graphs; Long term memory; Reading comprehension; Semantic primings; Spreading activations; State of the art; Unsupervised approaches; Chemical activation(...) | We propose an unsupervised approach for assessing conceptual complexity of texts, based on spreading activation. Using DBpedia knowledge graph as a proxy to long-term memory, mentioned concepts become activated and trigger further activation as the text is sequentially traversed. Drawing inspiration from psycholinguistic theories of reading comprehension, we model memory processes such as semantic priming, sentence wrap-up, and forgetting. We show that our models capture various aspects of conce(...) | ACL | 2020 | - | Hulpus I., Štajner S., Stuckenschmidt H. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084068774&partnerID=40&md5=f03a787428be66767cfcf9de97df3a56 | Germany | text classification | validation research | technique | - |
| Conference Paper | A State-Transition Framework to Answer Complex Questions over Knowledge Base | Knowledge based systems; Semantics; Complex questions; Knowledge basis; Knowledge graphs; Natural language questions; Primitive operations; Semantic query; State of the art; State transitions; Natural language processing systems(...) | Although natural language question answering over knowledge graphs have been studied in the literature, existing methods have some limitations in answering complex questions. To address that, in this paper, we propose a State Transition-based approach to translate a complex natural language question N to a semantic query graph (SQG) QS, which is used to match the underlying knowledge graph to find the answers to question N. In order to generate QS, we propose four primitive operations (expand, f(...) | ACL | 2020 | - | Hu S., Zou L., Zhang X. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85066476875&partnerID=40&md5=91bc65a67dcc9579224479b7cba5f2cb | China | question answering | validation research | technique | - |
| Conference Paper | A Study of Pre-Trained Language Models in Natural Language Processing | BERT; Cross-modal; Embedding; KG; Natural Language Generation; Pre-trained(...) | Pre-trained Language Model (PLM) is a very popular topic in natural language processing (NLP). It is the rapid development of pre-trained language models (PLMs) that has led to the achievements of natural language today. In this article, we give a review of important PLMs. First, we generally introduce the development history and achievements of PLMs. Second, we present several extraordinary PLMs, including BERT, the variants of BERT, Multimodal PLMs, PLMs combined with Knowledge Graph and PLMs (...) | IEEE | 2020 | 10.1109/smartcloud49737.2020.00030 | Duan J., Zhao H., Zhou Q., Qiu M., Liu M. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098480175&doi=10.1109%2fSmartCloud49737.2020.00030&partnerID=40&md5=4a4a4dbd5141f5b890522c963ff07ad1 | China, United States | augmented language models, text generation | secondary research | guidelines | - |
| Conference Paper | A Survey of Embedding Models of Entities and Relationships for Knowledge Graph Completion | - | Knowledge graphs (KGs) of real-world facts about entities and their relationships are useful resources for a variety of natural language processing tasks. However, because knowledge graphs are typically incomplete, it is useful to perform knowledge graph completion or link prediction, i.e. predict whether a relationship not in the knowledge graph is likely to be true. This paper serves as a comprehensive survey of embedding models of entities and relationships for knowledge graph completion, sum(...) | ACL | 2020 | 10.18653/v1/2020.textgraphs-1.1 | Nguyen, Dat Quoc | https://aclanthology.org/2020.textgraphs-1.1 | Vietnam | knowledge graph embedding, link prediction | secondary research | guidelines | - |
| Journal Article | A3Id: an Automatic and Interpretable Implicit Interference Detection Method for Smart Home Via Knowledge Graph | Interference detection; knowledge graph; natural language processing (NLP); smart home(...) | The smart home brings together devices, the cloud, data, and people to make home living more comfortable and safer. Trigger-action programming enables users to connect smart devices using if-this-then-that (IFTTT)-style rules. With the increasing number of devices in smart home systems, multiple running rules that act on actuators in contradictory ways may cause unexpected and unpredictable interference problems, which can put residents and their belongings at risk. Previous studies have conside(...) | IEEE | 2020 | 10.1109/jiot.2019.2959063 | Xiao D., Wang Q., Cai M., Zhu Z., Zhao W. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082106635&doi=10.1109%2fJIOT.2019.2959063&partnerID=40&md5=494b3ef4dfee8edbbda1c1e426044f56 | China | entity extraction, relation extraction, semantic search | validation research | method | information technology |
| Conference Paper | Active Learning Based Relation Classification for Knowledge Graph Construction from Conversation Data | Active learning; Deep learning; Knowledge Graph; Relation classification(...) | Creation of a Knowledge Graph (KG) from text, and its usages in solving several Natural Language Processing (NLP) problems are emerging research areas. Creating KG from text is a challenging problem which requires several NLP modules working together in unison. This task becomes even more challenging when constructing knowledge graph from a conversational data, as user and agent stated facts in conversations are often not grounded and can change with dialogue turns. In this paper, we explore KG (...) | Scopus | 2020 | 10.1007/978-3-030-63820-7_70 | Ahmad Z., Ekbal A., Sengupta S., Mitra A., Rammani R., Bhattacharyya P. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097300161&doi=10.1007%2f978-3-030-63820-7_70&partnerID=40&md5=c9f91fb7871307866236c0f9b727756f | India | entity extraction, relation extraction, relation classification | validation research | technique | - |
| Conference Paper | Adapting Meta Knowledge Graph Information for Multi-Hop Reasoning over Few-Shot Relations | Knowledge representation; High frequency HF; Knowledge graphs; Meta-knowledge; Meta-parameters; Query answering; Reasoning methods; Reasoning models; State-of-the-art methods; Natural language processing systems(...) | Multi-hop knowledge graph (KG) reasoning is an effective and explainable method for predicting the target entity via reasoning paths in query answering (QA) task. Most previous methods assume that every relation in KGs has enough training triples, regardless of those few-shot relations which cannot provide sufficient triples for training robust reasoning models. In fact, the performance of existing multi-hop reasoning methods drops significantly on few-shot relations. In this paper, we propose a(...) | ACL | 2020 | - | Lv X., Gu Y., Han X., Hou L., Li J., Liu Z. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078244531&partnerID=40&md5=218cfe38a23eee703309d3a67597207f | China | link prediction | validation research | tool | - |
| Conference Paper | Adverse Drug Event Prediction Using Noisy Literature-Derived Knowledge Graphs | Adverse drug event; Deep learning; Knowledge graph embeddings(...) | Adverse Drug Events (ADEs) are drug side-effects that are not known during clinical trials and cause substantial clinical and economic burden globally. A wealth of potential causal associations, that facilitate ADE discovery, lie in the growing body of biomedical literature, from which knowledge graphs - where vertices and edges represent clinical concepts and their relations - can be inferred using Natural Language Processing (NLP). State-of-the-art literature-based ADE prediction models employ(...) | Scopus | 2020 | - | Lim A., Mariappan R., Rajan V. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103448748&partnerID=40&md5=3b77cb2d304d03fc335c51b05f3d1c2e | Singapore | knowledge graph embedding | validation research | method | health |
| Conference Paper | Ai 2000: a Decade of Artificial Intelligence | Artificial Intelligence; Data Mining; Machine Learning; Most Influential Scholars(...) | In the past decades, artificial intelligence has dramatically changed the way we work and live. Moreover, it is increasingly becoming a national strategy for its rapid development and broad application in industries. However, the way artificial intelligence advances itself is sorely lacking until now. One of the most important reasons is the deficiency of timely and reliable knowledge graph in this field. To illustrate the problem, we introduce an academic knowledge graph of AI, named AI 2000, w(...) | ACM | 2020 | 10.1145/3394231.3397925 | Shao Z., Shen Z., Yuan S., Tang J., Wang Y., Wu L., Zheng W. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088390761&doi=10.1145%2f3394231.3397925&partnerID=40&md5=0beb2d8029f90c3ffef149bf0c59e569 | China | semantic search | solution proposal | tool; resource | scholarly domain |
| Conference Paper | Ai-Kg: an Automatically Generated Knowledge Graph of Artificial Intelligence | Artificial Intelligence; Information Extraction; Knowledge graph; Natural Language Processing; Scholarly data(...) | Scientific knowledge has been traditionally disseminated and preserved through research articles published in journals, conference proceedings, and online archives. However, this article-centric paradigm has been often criticized for not allowing to automatically process, categorize, and reason on this knowledge. An alternative vision is to generate a semantically rich and interlinked description of the content of research publications. In this paper, we present the Artificial Intelligence Knowl(...) | Scopus | 2020 | 10.1007/978-3-030-62466-8_9 | Dessì D., Osborne F., Reforgiato Recupero D., Buscaldi D., Motta E., Sack H. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096591173&doi=10.1007%2f978-3-030-62466-8_9&partnerID=40&md5=944ff4e0c33e5e22c91dff8992df7b9e | Germany, France, United Kingdom, Italy | entity extraction, relation extraction, ontology construction | validation research | method; resource | scholarly domain |
| Conference Paper | An Ai Chatbot for the Museum Based on User Interaction over a Knowledge Base | Knowledge Graph; Multi Round Human-Machine Interaction; NLP; Voice Search(...) | Recently, with the advancement of technologies in AI and Knowledge Base, several museums are using chatbots for visitors. One of the problems with these technologies, however is that gradually tends to be of no real interest to visitors owing to the lack of significant interaction, this eventually distracts visitors from experiencing the exhibits. In the demo, we present AIMuBot, an interactive system for searching the information from the museum's knowledge base with natural language. The syste(...) | ACM | 2020 | 10.1145/3421766.3421888 | Zhou C., Sinha B., Liu M. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095817900&doi=10.1145%2f3421766.3421888&partnerID=40&md5=35d6ec02f94f35b54eddba83b8ae3f08 | China | conversational interfaces | solution proposal | tool | culture |
| Conference Paper | An Auto Question Answering System for Tree Hole Rescue | Automatic question answering; Knowledge graph; Natural language processing; Tree Hole Rescue; Word embedding(...) | This paper introduces an automatic question answering system which aimed to provide online how-to instructions for volunteers of Tree Hole Rescue–a Chinese online suicide rescue organization. When a volunteer needs to make sure how to deal with a rescue task professionally, he/she could ask this system via its WeChat public account other than reading a rescue instruction menu book. Firstly, a Tree Hole Rescue question-answer knowledge graph was constructed to manage Tree Hole Rescue question-ans(...) | Scopus | 2020 | 10.1007/978-3-030-61951-0_2 | Wang F., Li Y. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094138128&doi=10.1007%2f978-3-030-61951-0_2&partnerID=40&md5=aa49e698e3f4afbea1f6d0f1fd5083e6 | China | question answering | solution proposal | method | health |
| Conference Paper | An Efficient Application Searching Approach Based on User Review Knowledge Graph | App searching; Knowledge-graph; NLP(...) | Finding a software application that perfectly suits user needs is essential for improving user experiences, as well as contributing to the development of the application ecosystems. However, it is not an easy task regarding the huge number of existing applications that are available for use. In this paper, we propose to tackle this challenge by exploring valuable information from user reviews. In particular, we design a user review knowledge graph that consists of both functional information and(...) | Scopus | 2020 | 10.18293/seke2020-119 | Li F., Li T. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090507011&doi=10.18293%2fSEKE2020-119&partnerID=40&md5=a075aa04e9bd01e670516ea4cb6c9382 | China | entity extraction, relation extraction, semantic search | validation research | method | information technology |
| Conference Paper | An Interactive System for Knowledge Graph Search | Database systems; Human computer interaction; Knowledge representation; Natural language processing systems; Query processing; Graphical interface; Interactive system; Knowledge graphs; Natural language queries; Natural languages; Query processing engine; Rapid growth; Real-world; Search engines(...) | Recent years, knowledge graphs (KG) have experienced rapid growth since they contain enormous volume of facts about the real world, and become the source of various knowledge. It is hence highly desirable that the query-processing engine of a KG is capable of processing queries presented in natural language directly, though these natural language queries bring various ambiguities. In this paper, we present, an interactive system for searching information from knowledge graphs with natural langua(...) | Scopus | 2020 | 10.1007/978-3-030-59419-0_52 | Baivab S., Wang X., Jiang W., Ma J., Zhan H., Zhong X. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092109956&doi=10.1007%2f978-3-030-59419-0_52&partnerID=40&md5=43f5ea05aa3345a51fe8365ef8c8ef7b | China | semantic search | solution proposal | tool | - |
| Conference Paper | An Unsupervised Joint System for Text Generation from Knowledge Graphs and Semantic Parsing | Computational linguistics; Graphic methods; Semantics; Different domains; Domain specific; Graph parsing; Joint system; Knowledge extraction; Knowledge graphs; Large amounts; Semantic parsing; Text data; Text generations; Knowledge graph(...) | Knowledge graphs (KGs) can vary greatly from one domain to another. Therefore supervised approaches to both graph-to-text generation and text-to-graph knowledge extraction (semantic parsing) will always suffer from a shortage of domain-specific parallel graph-text data; at the same time, adapting a model trained on a different domain is often impossible due to little or no overlap in entities and relations. This situation calls for an approach that (1) does not need large amounts of annotated da(...) | ACL | 2020 | - | Schmitt M., Sharifzadeh S., Tresp V., Schütze H. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100496876&partnerID=40&md5=101d4042f2154ace4ce11f4a68a0b908 | Germany | text generation, semantic parsing | validation research | tool | - |
| Conference Paper | Are You for Real Detecting Identity Fraud Via Dialogue Interactions | Crime; Heuristic methods; Knowledge representation; Speech processing; Speech recognition; Dialogue management; Dialogue strategy; Financial industry; Knowledge graphs; Personal information; Problem analysis; Real-world scenario; Recognition accuracy; Natural language processing systems(...) | Identity fraud detection is of great importance in many real-world scenarios such as the financial industry. However, few studies addressed this problem before. In this paper, we focus on identity fraud detection in loan applications and propose to solve this problem with a novel interactive dialogue system which consists of two modules. One is the knowledge graph (KG) constructor organizing the personal information for each loan applicant. The other is structured dialogue management that can dy(...) | ACL | 2020 | - | Wang W., Zhang J., Li Q., Zong C., Li Z. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084292925&partnerID=40&md5=fbfea3931e12234683c152c01d783bde | China | conversational interfaces | validation research | tool | law |
| Conference Paper | Automatic Taxonomy Induction and Expansion | HTTP; Taxonomies; Distributional semantics; End to end; Hybrid approach; Induction system; Knowledge graphs; Linguistic patterns; Natural language processing systems(...) | The Knowledge Graph Induction Service (KGIS) is an end-to-end knowledge induction system. One of its main capabilities is to automatically induce taxonomies1 from input documents using a hybrid approach that takes advantage of linguistic patterns, semantic web and neural networks. KGIS allows the user to semi-automatically curate and expand the induced taxonomy through a component called smart spreadsheet by exploiting distributional semantics. In this paper, we describe these taxonomy induction(...) | ACL | 2020 | - | Fauceglia N.R., Gliozzo A., Dash S., Chowdhury M.F.M., Mihindukulasooriya N. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087435953&partnerID=40&md5=e6a757718ea22a1913f24ed788ad3a26 | United States | entity extraction, relation extraction, ontology construction | solution proposal | tool | - |
| Conference Paper | Auxiliary Decision Technology and Application of Power Grid Fault Disposal Based on Knowledge Understanding of Fault Preplan | auxiliary decision-making; fault disposal; fault preplan; knowledge graph; natural language processing(...) | Combined with the characteristics of power grid fault disposal, auxiliary decision-making technology and implementation architecture of power grid fault disposal that based on knowledge understanding of fault preplan are proposed. Natural language processing technology is used to structurally extract key information of the fault disposal preplan. On this basis, a fault disposal knowledge graph is established. The method can realize the intelligent decision-making and disposal of faults by online(...) | IEEE | 2020 | 10.1109/icpre51194.2020.9233245 | Bo W., Jian N., Mei D.Z., Yong Z., Shan X., Changming J., Zhe Z., Tingxiang L., Yiming Z. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096647988&doi=10.1109%2fICPRE51194.2020.9233245&partnerID=40&md5=be648fec858e03b69701fb8765afc12e | China | entity extraction, relation extraction, semantic search | solution proposal | method | energy |
| Conference Paper | Ba-Ikg: Bilstm Embedded Albert for Industrial Knowledge Graph Generation and Reuse | entity relation extraction; industrial knowledge graph; knowledge modeling; knowledge question and answer(...) | As the industrial production mode is shifting towards digitalization and intelligence in the new era. Enterprises put forward higher requirements for efficient processing and utilization of accumulated unstructured data. At present, the knowledge and data contained in a large number of unstructured documents are scattered. The types of entities and relationships are diverse. And the constraints of production rules are complicated, which increases the difficulty of knowledge management and utiliz(...) | IEEE | 2020 | 10.1109/indin45582.2020.9442198 | Zhou B., Bao J., Liu Y., Song D. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111099965&doi=10.1109%2fINDIN45582.2020.9442198&partnerID=40&md5=e4d4bd82a21597be8e27caab0a594a33 | China | entity extraction, relation extraction, semantic search, question answering | solution proposal | method | engineering |
| Conference Paper | Bakgrastec: a Background Knowledge Graph Based Method for Short Text Classification | Attention mechanism; Graph neural network; Knowledge graph; Short text(...) | Short text classification is an important task in the area of natural language processing. Recent studies attempt to employ external knowledge to improve classification performance, but they ignore the correlation between external knowledge and have poor interpretability. This paper proposes a novel Background Knowledge Graph based method for Short Text Classification called BaKGraSTeC for short, which can not only employ external knowledge from a knowledge graph to enrich text information, but (...) | IEEE | 2020 | 10.1109/icbk50248.2020.00058 | Jiang X., Shen Y., Wang Y., Jin X., Cheng X. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092540204&doi=10.1109%2fICBK50248.2020.00058&partnerID=40&md5=28597b162dad397d1f03954097471c75 | China | text classification | validation research | technique | - |
| Conference Paper | Barack'S Wife Hillary: Using Knowledge Graphs for Fact-Aware Language Modeling | Computational linguistics; Factual knowledge; Human language; Knowledge graphs; Language model; Training time; Modeling languages(...) | Modeling human language requires the ability to not only generate fluent text but also encode factual knowledge. However, traditional language models are only capable of remembering facts seen at training time, and often have difficulty recalling them. To address this, we introduce the knowledge graph language model (KGLM), a neural language model with mechanisms for selecting and copying facts from a knowledge graph that are relevant to the context. These mechanisms enable the model to render i(...) | ACL | 2020 | - | Logan R.L., IV, Liu N.F., Peters M.E., Gardner M., Singh S. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084035303&partnerID=40&md5=bae32a9cf40bc8c2a3755d5922ecfdb9 | United States | augmented language models | validation research | tool; resource | - |
| Journal Article | Bert+Vnkg: Using Deep Learning and Knowledge Graph to Improve Vietnamese Question Answering System | Bidirectional encoder representation from transformer (BERT); Deep learning; Knowledge graph; Long short-term memory (LSTM); Natural language processing; Question answering (QA); Vietnamese tourism(...) | A question answering (QA) system based on natural language processing and deep learning is a prominent area and is being researched widely. The Long Short-Term Memory (LSTM) model that is a variety of Recurrent Neural Network (RNN) used to be popular in machine translation, and question answering system. However, that model still has certainly limited capabilities, so a new model named Bidirectional Encoder Representation from Transformer (BERT) emerged to solve these restrictions. BERT has more(...) | Scopus | 2020 | 10.14569/ijacsa.2020.0110761 | Phan T.H.V., Do P. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088985283&doi=10.14569%2fIJACSA.2020.0110761&partnerID=40&md5=a42476a896ca180e281da94e02c20014 | Vietnam | augmented language models, question answering | validation research | technique | tourism |
| Conference Paper | Bert-Mk: Integrating Graph Contextualized Knowledge into Pre-Trained Language Models | Computational linguistics; Knowledge representation; Topology; Contextualized knowledge; Knowledge graphs; Knowledge-representation; Language model; Learning methods; Structure of knowledge; Subgraphs; Topological structure; Traditional knowledge; Training unit; Knowledge graph(...) | Complex node interactions are common in knowledge graphs (KGs), and these interactions can be considered as contextualized knowledge exists in the topological structure of KGs. Traditional knowledge representation learning (KRL) methods usually treat a single triple as a training unit, neglecting the usage of graph contextualized knowledge. To utilize these unexploited graph-level knowledge, we propose an approach to model subgraphs in a medical KG. Then, the learned knowledge is integrated with(...) | ACL | 2020 | - | He B., Zhou D., Xiao J., Jiang X., Liu Q., Yuan N.J., Xu T. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106741988&partnerID=40&md5=6fb832505647133fbe428aaf69cbcd20 | China | augmented language models | validation research | technique | health |
| Conference Paper | Big Green at Wnut 2020 Shared Task-1: Relation Extraction as Contextualized Sequence Classification | - | Relation and event extraction is an important task in natural language processing. We introduce a system which uses contextualized knowledge graph completion to classify relations and events between known entities in a noisy text environment. We report results which show that our system is able to effectively extract relations and events from a dataset of wet lab protocols.(...) | ACL | 2020 | 10.18653/v1/2020.wnut-1.36 | Miller, Chris and Vosoughi, Soroush | https://aclanthology.org/2020.wnut-1.36 | United States | relation extraction | validation research | technique | - |
| Conference Paper | Biomedical Event Extraction with Hierarchical Knowledge Graphs | Complex networks; Computational linguistics; Extraction; Graphic methods; Natural language processing systems; Biomolecular interactions; Complex events; Domain knowledge; Events extractions; Graph edges; Hierarchical graph representations; Hierarchical knowledge; Knowledge graphs; Language model; Unified medical language systems; Knowledge graph(...) | Biomedical event extraction is critical in understanding biomolecular interactions described in scientific corpus. One of the main challenges is to identify nested structured events that are associated with non-indicative trigger words. We propose to incorporate domain knowledge from Unified Medical Language System (UMLS) to a pre-trained language model via a hierarchical graph representation encoded by a proposed Graph Edge-conditioned Attention Networks (GEANet). To better recognize the trigge(...) | ACL | 2020 | - | Huang K.-H., Yang M., Peng N. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109732300&partnerID=40&md5=b63ec3f726a966a65e8732cea4e2f988 | United States | entity extraction, relation extraction | validation research | technique | health |
| Journal Article | Building a Knowledge Graph by Using Cross-Lingual Transfer Method and Distributed Minie Algorithm on Apache Spark | Cross-lingual transfer method; Distributed MinIE; Knowledge graph; Natural language processing; Triples extraction(...) | The simplest and effective way to store human knowledge through centuries was using text. Along with the advancement of technology nowadays, the volume of text has grown to be larger and larger. To extract useful information from this amount of text becomes an exceptionally complex task. As an effort to solve that problem, in this paper, we present a pipeline to extract core knowledge from large quantity text using distributed computing. The components of our pipeline are systems that were known(...) | Scopus | 2020 | 10.1007/s00521-020-05495-1 | Do P., Phan T., Le H., Gupta B.B. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096524024&doi=10.1007%2fs00521-020-05495-1&partnerID=40&md5=83d0cfd07ec524a4d0e1814edcfea6a1 | United Kingdom, India, Vietnam | entity extraction, relation extraction | validation research | method | - |
| Conference Paper | Challenges of Knowledge Graph Evolution from an Nlp Perspective | Cultural Heritage; Knowledge Graph Evolution; NLP(...) | Knowledge graphs often express static facts, but concepts and entities change over time. In this position paper, we propose challenges that arise from the perspective of combining NLP and KG evolution in the digital humanities domain based on preliminary experiments. Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).(...) | Scopus | 2020 | - | Tietz T., Alam M., Sack H., van Erp M. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095966893&partnerID=40&md5=c106c73240fa7ea6a88041fe790f1359 | Germany, Netherlands | error detection | opinion paper | guidelines | - |
| Conference Paper | Collaborative Policy Learning for Open Knowledge Graph Reasoning | Knowledge representation; Linguistics; Open Data; Reinforcement learning; Collaborative agents; Collaborative policy; Fact extraction; Knowledge graphs; On the flies; Reasoning methods; Search spaces; Source codes; Natural language processing systems(...) | In recent years, there has been a surge of interests in interpretable graph reasoning methods. However, these models often suffer from limited performance when working on sparse and incomplete graphs, due to the lack of evidential paths that can reach target entities. Here we study open knowledge graph reasoning-a task that aims to reason for missing facts over a graph augmented by a background text corpus. A key challenge of the task is to filter out “irrelevant” facts extracted from corpus, in(...) | ACL | 2020 | - | Fu C., Chen T., Qu M., Jin W., Ren X. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084325498&partnerID=40&md5=e577b4d5a3073ec43306f199953c4c7d | Canada, China, United States | triple classification | validation research | tool | - |
| Conference Paper | Combining Embedding Methods for a Word Intrusion Task | Embeddings; Knowledge representation; Byte-pair encoding; Embedding method; Individual modeling; Knowledge graphs; Sub words; Natural language processing systems(...) | We report a new baseline for a Danish word intrusion task by combining pre-trained off-the-shelf word, subword and knowledge graph embedding models. We test fastText, Byte-Pair Encoding, BERT and the knowledge graph embedding in Wembedder, finding fastText as the individual model with the superior performance, while a simple combination of the fastText with other models can slightly improve the accuracy of finding the odd-one-out words in the word intrusion task. © 2020 German Society for Comput(...) | Scopus | 2020 | - | Nielsen F.Å., Hansen L.K. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103456037&partnerID=40&md5=092fa30e2c207e995d08d3a929692aba | Denmark | augmented language models, knowledge graph embedding | solution proposal | technique | - |
| Conference Paper | Combining Knowledge Graph Embedding and Network Embedding for Detecting Similar Mobile Applications | Iterative methods; Knowledge representation; Metadata; Mobile computing; Natural language processing systems; Security of data; Semantics; Embedding method; Embedding strategies; Knowledge graphs; Lightweight ontology; Mobile applications; Network embedding; Semantic representation; Unstructured texts; Embeddings(...) | With the popularity of mobile devices, large amounts of mobile applications (a.k.a.“app”) have been developed and published. Detecting similar apps from a large pool of apps is a fundamental and important task because it has many benefits for various purposes. There exist several works that try to combine different metadata of apps for measuring the similarity between apps. However, few of them pay attention to the roles of this service. Besides, existing methods do not distinguish the character(...) | Scopus | 2020 | 10.1007/978-3-030-60450-9_21 | Li W., Zhang B., Xu L., Wang M., Luo A., Niu Y. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093101846&doi=10.1007%2f978-3-030-60450-9_21&partnerID=40&md5=d5671a86298ad1ac1128a9f544edc26e | China | knowledge graph embedding, semantic search | validation research | method | information technology |
| Conference Paper | Comet: Commonsense Transformers for Automatic Knowledge Graph Construction | Computational linguistics; Knowledge based systems; Commonsense knowledge; Explicit knowledge; Generative model; Human performance; Implicit knowledge; Knowledge graphs; Knowledge-base construction; Natural languages; Modeling languages(...) | We present the first comprehensive study on automatic knowledge base construction for two prevalent commonsense knowledge graphs: ATOMIC (Sap et al., 2019) and ConceptNet (Speer et al., 2017). Contrary to many conventional KBs that store knowledge with canonical templates, commonsense KBs only store loosely structured open-text descriptions of knowledge. We posit that an important step toward automatic commonsense completion is the development of generative models of commonsense knowledge, and p(...) | ACL | 2020 | - | Bosselut A., Rashkin H., Sap M., Malaviya C., Celikyilmaz A., Choi Y. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084040907&partnerID=40&md5=f7e9dc21d41a91b6d7646b53e4952c17 | United States | entity classification, link prediction, augmented language models | validation research | tool | - |
| Conference Paper | Cometa: a Corpus for Medical Entity Linking in the Social Media | Computational linguistics; Social networking (online); Terminology; Benchmark experiments; Complex nature; Knowledge graphs; Medical knowledge; Performance gaps; Property; SNOMED-CT; Social media; Knowledge graph(...) | Whilst there has been growing progress in Entity Linking (EL) for general language, existing datasets fail to address the complex nature of health terminology in layman's language. Meanwhile, there is a growing need for applications that can understand the public's voice in the health domain. To address this we introduce a new corpus called COMETA, consisting of 20k English biomedical entity mentions from Reddit expert-annotated with links to SNOMED CT, a widely-used medical knowledge graph. Our(...) | ACL | 2020 | - | Basaldella M., Liu F., Shareghi E., Collier N. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104050301&partnerID=40&md5=171cac0eda6616793ff18f4f90b8ac14 | United Kingdom | entity linking | validation research | resource | social media; health |
| Conference Paper | Commonsense Evidence Generation and Injection in Reading Comprehension | Computational linguistics; Semantics; 'current; Commonsense knowledge; Commonsense reasoning; High-accuracy; Knowledge graphs; Language model; Linguistic units; Reading comprehension; Reasoning models; Semantic relationships; Knowledge graph(...) | Human tackle reading comprehension not only based on the given context itself but often rely on the commonsense beyond. To empower the machine with commonsense reasoning, in this paper, we propose a Commonsense Evidence Generation and Injection framework in reading comprehension, named CEGI. The framework injects two kinds of auxiliary commonsense evidence into comprehensive reading to equip the machine with the ability of rational thinking. Specifically, we build two evidence generators: one ai(...) | ACL | 2020 | - | Liu Y., Yang T., You Z., Fan W., Yu P.S. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112400683&partnerID=40&md5=beb4c3d7e5ce103f367754e3f5ac9179 | United States | natural language inference, question answering | validation research | method | - |
| Conference Paper | Conceptbert: Concept-Aware Representation for Visual Question Answering | Computational linguistics; Natural language processing systems; Visual languages; 'current; Common sense; Direct analysis; Factual knowledge; Knowledge graphs; Modal representation; Multi-modal; Natural languages; Question Answering; Visual elements; Knowledge graph(...) | Visual Question Answering (VQA) is a challenging task that has received increasing attention from both the computer vision and the natural language processing communities. Current works in VQA focus on questions which are answerable by direct analysis of the question and image alone. We present a concept-aware algorithm, ConceptBert, for questions which require common sense, or basic factual knowledge from external structured content. Given an image and a question in natural language, ConceptBer(...) | ACL | 2020 | - | Gardères F., Ziaeefard M., Abeloos B., Lecue F. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118430585&partnerID=40&md5=8fe1049edc7798b913b187323616fe35 | Canada, France | question answering, augmented language models | validation research | tool | - |
| Conference Paper | Conquest: a Framework for Building Template-Based Iqa Chatbots for Enterprise Knowledge Graphs | ChatBot; Interactive Question Answering; Knowledge Graph; Linked Data(...) | The popularization of Enterprise Knowledge Graphs (EKGs) brings an opportunity to use Question Answering Systems to consult these sources using natural language. We present CONQUEST, a framework that automates much of the process of building chatbots for the Template-Based Interactive Question Answering task on EKGs. The framework automatically handles the processes of construction of the Natural Language Processing engine, construction of the question classification mechanism, definition of the(...) | Scopus | 2020 | 10.1007/978-3-030-51310-8_6 | Avila C.V.S., Franco W., Maia J.G.R., Vidal V.M.P. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087530549&doi=10.1007%2f978-3-030-51310-8_6&partnerID=40&md5=90b1cf371558c57ff7751490c108e38c | Brazil | question answering, conversational interfaces | solution proposal | tool | - |
| Journal Article | Constructing Knowledge Graphs and Their Biomedical Applications | knowledge graphs, Network embeddings, Text mining, Natural language processing, Machine learning, Lterature review(...) | Knowledge graphs can support many biomedical applications. These graphs represent biomedical concepts and relationships in the form of nodes and edges. In this review, we discuss how these graphs are constructed and applied with a particular focus on how machine learning approaches are changing these processes. Biomedical knowledge graphs have often been constructed by integrating databases that were populated by experts via manual curation, but we are now seeing a more robust use of automated s(...) | ScienceDirect | 2020 | 10.1016/j.csbj.2020.05.017 | David N. Nicholson and Casey S. Greene | https://www.sciencedirect.com/science/article/pii/S2001037020302804 | United States | relation extraction, semantic search | secondary research | guidelines | health |
| Conference Paper | Construction of Knowledge Graphs for Video Lectures | Artificial Intelligence; Knowledge Graph; Linked Open Data; Natural Language Processing; video lectures(...) | Knowledge Graphs (KG) have become very important in representing both structured and unstructured data. Knowledge graphs are penetrating our daily lives, be it intelligent voice assistants or Facebook friend search. In this research paper, we are focusing on how Knowledge Graphs can be constructed for a video lecture and list down the various important steps that are involved in the process of construction of the graph. Knowledge Graphs are a way of modelling a knowledge domain programmatically (...) | IEEE | 2020 | 10.1109/icaccs48705.2020.9074320 | Shanmukhaa G.S., Nandita S.K., Kiran M.V.K. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084682474&doi=10.1109%2fICACCS48705.2020.9074320&partnerID=40&md5=bc18fafd6ca8c429eb3a8d292793bec2 | India | entity extraction, relation extraction, semantic search | solution proposal | method | education |
| Conference Paper | Creation and Enrichment of a Terminological Knowledge Graph in the Legal Domain | Knowledge Graphs; Linguistic Linked Data; Semantic Web; Terminology Management(...) | Domain-specific terminologies are of great use in a number of contexts, such as information retrieval from text documents or supporting humans in translation tasks. However, automated terminology extraction tools usually render plain lists with no additional information (hierarchical relations, definitions or examples of use, amongst others). The output of these tools is very often offered in non-open formats, hampering their reuse and interoperability. Moreover, terminology management tools dem(...) | Scopus | 2020 | - | Martín-Chozas P. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084616018&partnerID=40&md5=bcb915526ea38ea83a305eab069c6d96 | Spain | relation extraction, error detection | solution proposal | method | law |
| Conference Paper | Creative Storytelling with Language Models and Knowledge Graphs | Knowledge graph; Language model; Natural language generation; Story generation(...) | Automated story generation is a popular and well-recognized task in the field of natural language processing. The emergence of pre-trained language models based on large Transformer architectures shows the great capability of text generation. However, language models are limited when the generation requires explicit clues within the context. In this research, we study how to combine knowledge graphs with language models, and build a creative story generation system named DICE. DICE uses external(...) | Scopus | 2020 | - | Yang X., Tiddi I. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097546218&partnerID=40&md5=3914a7c1b9edb0fdd9c889dd3a8ed304 | Netherlands | text generation, augmented language models | validation research | tool | - |
| Conference Paper | Creativity Embedding: a Vector to Characterise and Classify Plausible Triples in Deep Learning Nlp Models | BERT; Creativity embedding; Creativity evaluation; Creativity metric; Knowledge graph; NLP; Triple(...) | In this paper we define the creativity embedding of a text based on four self-assessment creativity metrics, namely diversity, novelty, serendipity and magnitude, knowledge graphs, and neural networks. We use as basic unit the notion of triple (head, relation, tail). We investigate if additional information about creativity improves natural language processing tasks. In this work, we focus on triple plausibility task, exploiting BERT model and a WordNet11 dataset sample. Contrary to our hypothes(...) | Scopus | 2020 | - | Oliveri I., Ardito L., Rizzo G., Morisio M. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097864462&partnerID=40&md5=a6d41c3076df8406bafc12883a8154da | Italy | triple classification, augmented language models | solution proposal | technique | - |
| Conference Paper | Cyclegt: Unsupervised Graph-To-Text and Text-To-Graph Generation Via Cycle Training | - | Two important tasks at the intersection of knowledge graphs and natural language processing are graph-to-text (G2T) and text-tograph (T2G) conversion. Due to the difficulty and high cost of data collection, the supervised data available in the two fields are usually on the magnitude of tens of thousands, for example, 18K in the WebNLG 2017 dataset after preprocessing, which is far fewer than the millions of data for other tasks such as machine translation. Consequently, deep learning models for (...) | ACL | 2020 | - | Guo, Qipeng and Jin, Zhijing and Qiu, Xipeng and Zhang, Weinan and Wipf, David and Zhang, Zheng | https://aclanthology.org/2020.webnlg-1.8 | China | entity extraction, relation extraction, data-to-text generation | validation research | method | - |
| Journal Article | Denert-Kg: Named Entity and Relation Extraction Model Using Dqn, Knowledge Graph, and Bert | BERT; DQN; Knowledge graph; Named entity recognition; Relation extraction(...) | Along with studies on artificial intelligence technology, research is also being carried out actively in the field of natural language processing to understand and process people's language, in other words, natural language. For computers to learn on their own, the skill of understanding natural language is very important. There are a wide variety of tasks involved in the field of natural language processing, but we would like to focus on the named entity registration and relation extraction tas(...) | Scopus | 2020 | 10.3390/app10186429 | Yang S., Yoo S., Jeong O. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092060828&doi=10.3390%2fAPP10186429&partnerID=40&md5=1c93446f778509abe38cf70c4e55d18f | South Korea | entity extraction, relation extraction, augmented language models | validation research | technique | - |
| Journal Article | Disbot: a Portuguese Disaster Support Dynamic Knowledge Chatbot | Chatbots; Community resilience; Disaster management; Knowledge graphs; Natural language processing; Situational awareness(...) | This paper presents DisBot, the first Portuguese speaking chatbot that uses social media retrieved knowledge to support citizens and first-responders in disaster scenarios, in order to improve community resilience and decision-making. It was developed and tested using Design Science Research Methodology (DSRM), being progressively matured with field specialists through several design and development iterations. DisBot uses a state-of-the-art Dual Intent Entity Transformer (DIET) architecture to (...) | Scopus | 2020 | 10.3390/app10249082 | Boné J., Ferreira J.C., Ribeiro R., Cadete G. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098055192&doi=10.3390%2fapp10249082&partnerID=40&md5=ba86e8919d8fffabf70521c05b368405 | Portugal | conversational interfaces | validation research | tool | social media; public sector |
| Conference Paper | Discovering Knowledge Graph Schema from Short Natural Language Text Via Dialog | Computational linguistics; Active Learning; Dialogue strategy; Generalized binary searches; Knowledge graphs; Language statements; Multi-turn; Natural languages; Natural languages texts; Uncertainty; Uncertainty samplings; Knowledge graph(...) | We study the problem of schema discovery for knowledge graphs. We propose a solution where an agent engages in multi-turn dialog with an expert for this purpose. Each minidialog focuses on a short natural language statement, and looks to elicit the expert's desired schema-based interpretation of that statement, taking into account possible augmentations to the schema. The overall schema evolves by performing dialog over a collection of such statements. We take into account the probability that t(...) | ACL | 2020 | - | Ghosh S., Kundu A., Pramanick A., Bhattacharya I. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118464513&partnerID=40&md5=4f8afa6b778a6d12087b213c9aa77ae4 | India | conversational interfaces, ontology construction | validation research | method | - |
| Conference Paper | Distilling Structured Knowledge for Text-Based Relational Reasoning | - | There is an increasing interest in developing text-based relational reasoning systems, which are capable of systematically reasoning about the relationships between entities mentioned in a text. However, there remains a substantial performance gap between NLP models for relational reasoning and models based on graph neural networks (GNNs), which have access to an underlying symbolic representation of the text. In this work, we investigate how the structured knowledge of a GNN can be distilled in(...) | ACL | 2020 | 10.18653/v1/2020.emnlp-main.551 | Dong, Jin and Rondeau, Marc-Antoine and Hamilton, William L. | https://aclanthology.org/2020.emnlp-main.551 | Canada | augmented language models, natural language inference | validation research | technique | - |
| Journal Article | Drug Repurposing against Parkinson'S Disease by Text Mining the Scientific Literature | Data representation; Drug repurposing; Graph embedding; Knowledge representation learning; Machine learning; Parkinson's disease; Scientific literature; Text mining(...) | Purpose: Drug repurposing involves the identification of new applications for existing drugs. Owing to the enormous rise in the costs of pharmaceutical R&D, several pharmaceutical companies are leveraging repurposing strategies. Parkinson's disease is the second most common neurodegenerative disorder worldwide, affecting approximately 1–2 percent of the human population older than 65 years. This study proposes a literature-based drug repurposing strategy in Parkinson's disease. Design/methodolog(...) | Scopus | 2020 | 10.1108/lht-08-2019-0170 | Zhu Y., Jung W., Wang F., Che C. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083987300&doi=10.1108%2fLHT-08-2019-0170&partnerID=40&md5=3be7c9af2eb0fcefaa40d3ca03fec094 | China, United States | entity extraction, relation extraction, semantic search | validation research | method | health |
| Conference Paper | Drug-Drug Interaction Prediction on a Biomedical Literature Knowledge Graph | Drug-drug interactions; Knowledge discovery; Knowledge graph; Literature mining; Path analysis(...) | Knowledge Graphs provide insights from data extracted in various domains. In this paper, we present an approach discovering probable drug-to-drug interactions, through the generation of a Knowledge Graph from disease-specific literature. The Graph is generated using natural language processing and semantic indexing of biomedical publications and open resources. The semantic paths connecting different drugs in the Graph are extracted and aggregated into feature vectors representing drug pairs. A (...) | Scopus | 2020 | 10.1007/978-3-030-59137-3_12 | Bougiatiotis K., Aisopos F., Nentidis A., Krithara A., Paliouras G. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092241579&doi=10.1007%2f978-3-030-59137-3_12&partnerID=40&md5=c1433d47d57cebc5eb2e8e2938feb5f7 | Greece | link prediction, entity extraction, attribute extraction | validation research | tool | health |
| Conference Paper | Duality of Link Prediction and Entailment Graph Induction | Computational linguistics; Knowledge graphs; Link prediction; State of the art; Forecasting(...) | Link prediction and entailment graph induction are often treated as different problems. In this paper, we show that these two problems are actually complementary. We train a link prediction model on a knowledge graph of assertions extracted from raw text. We propose an entailment score that exploits the new facts discovered by the link prediction model, and then form entailment graphs between relations. We further use the learned entailments to predict improved link prediction scores. Our result(...) | ACL | 2020 | - | Hosseini M.J., Cohen S.B., Johnson M., Steedman M. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084090774&partnerID=40&md5=e55d3b1d597f291c3b3278c8d2e0014c | Australia, United Kingdom | link prediction | validation research | tool | - |
| Conference Paper | Embedding Imputation with Grounded Language Information | Computational linguistics; Graph algorithms; Graph structures; Vector spaces; Correlation coefficient; Critical problems; Graphical structures; Knowledge graphs; Language informations; Language processing; Natural languages; State of the art; Embeddings(...) | Due to the ubiquitous use of embeddings as input representations for a wide range of natural language tasks, imputation of embeddings for rare and unseen words is a critical problem in language processing. Embedding imputation involves learning representations for rare or unseen words during the training of an embedding model, often in a post-hoc manner. In this paper, we propose an approach for embedding imputation which uses grounded information in the form of a knowledge graph. This is in con(...) | ACL | 2020 | - | Yang Z., Zhu C., Sachidananda V., Darve E. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084066541&partnerID=40&md5=f3073d456559e02b92b4e75f708ba968 | United States | augmented language models | validation research | technique | - |
| Conference Paper | Enhancing Online Knowledge Graph Population with Semantic Knowledge | Data validation; Knowledge Graph; Relation extraction(...) | Knowledge Graphs (KG) are becoming essential to organize, represent and store the world’s knowledge, but they still rely heavily on humanly-curated structured data. Information Extraction (IE) tasks, like disambiguating entities and relations from unstructured text, are key to automate KG population. However, Natural Language Processing (NLP) methods alone can not guarantee the validity of the facts extracted and may introduce erroneous information into the KG. This work presents an end-to-end s(...) | Scopus | 2020 | 10.1007/978-3-030-62419-4_11 | Fernàndez-Cañellas D., Marco Rimmek J., Espadaler J., Garolera B., Barja A., Codina M., Sastre M., Giro-i-Nieto X., Riveiro J.C., Bou-Balust E. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096586450&doi=10.1007%2f978-3-030-62419-4_11&partnerID=40&md5=89e6fb51a6cf9c7f078e5c892263d42d | Spain | entity extraction, relation extraction | validation research | method | - |
| Conference Paper | Enhancing Question Answering by Injecting Ontological Knowledge through Regularization | - | Deep neural networks have demonstrated high performance on many natural language processing (NLP) tasks that can be answered directly from text, and have struggled to solve NLP tasks requiring external (e.g., world) knowledge. In this paper, we present OSCR (Ontology-based Semantic Composition Regularization), a method for injecting task-agnostic knowledge from an Ontology or knowledge graph into a neural network during pre-training. We evaluated the performance of BERT pre-trained on Wikipedia (...) | ACL | 2020 | 10.18653/v1/2020.deelio-1.7 | Goodwin, Travis and Demner-Fushman, Dina | https://aclanthology.org/2020.deelio-1.7 | United States | augmented language models, question answering | validation research | tool | - |
| Conference Paper | Enriching Bert with Knowledge Graph Embeddings for Document Classification | Embeddings; Knowledge representation; Metadata; Natural language processing systems; Classification tasks; Coarse-grained; Detailed classification; Document Classification; Knowledge graphs; Language model; Source codes; Text representation; Information retrieval systems(...) | In this paper, we focus on the classification of books using short descriptive texts (cover blurbs) and additional metadata. Building upon BERT, a deep neural language model, we demonstrate how to combine text representations with metadata and knowledge graph embeddings, which encode author information. Compared to the standard BERT approach we achieve considerably better results for the classification task. For a more coarse-grained classification using eight labels we achieve an F1-score of 87(...) | Scopus | 2020 | - | Ostendorff M., Bourgonje P., Berger M., Moreno-Schneider J., Rehm G., Gipp B. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098377698&partnerID=40&md5=fe0dd504aa6b69b57e285a77c3dd7e3d | Germany | text classification, knowledge graph embedding, augmented language models | validation research | tool | - |
| Conference Paper | Ent-Desc: Entity Description Generation by Exploring Knowledge Graph | Computational linguistics; Large dataset; Natural language processing systems; 'current; Graph information; Key-value pairs; Knowledge graphs; Language description; Large-scales; Natural languages; RDF triples; Sequence models; Text generations; Knowledge graph(...) | Previous works on knowledge-to-text generation take as input a few RDF triples or key-value pairs conveying the knowledge of some entities to generate a natural language description. Existing datasets, such as WIKIBIO, WebNLG, and E2E, basically have a good alignment between an input triple/pair set and its output text. However, in practice, the input knowledge could be more than enough, since the output description may only cover the most significant knowledge. In this paper, we introduce a lar(...) | ACL | 2020 | - | Cheng L., Wu D., Bing L., Zhang Y., Jie Z., Lu W., Si L. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098436623&partnerID=40&md5=8ab49be60e4ae7b308d8bbbec1cd8093 | Canada, China, Singapore | data-to-text generation | validation research | tool; resource | - |
| Conference Paper | Entity Hierarchical Clustering Method Based on Multi-Channel and T-Sne Dimension Reduction | BERT; Improved hierarchical clustering; Multi-channel; Network embedding; T-SNE(...) | Named entity clustering is a basic work in the field of natural language processing, which is helpful to excavate the implicit relationship between entities. Most of the existing clustering algorithms are unable to combine various features of entities and have some problems such as poor hierarchical clustering analysis. Based on this, this paper proposes a multi-channel dimensionless entity clustering method and carries out experimental verification. A multi-channel framework is constructed, and(...) | IEEE | 2020 | 10.1109/itaic49862.2020.9339166 | Feng H., Duan L., Liu S., Liu S. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101438555&doi=10.1109%2fITAIC49862.2020.9339166&partnerID=40&md5=698ae7f0dde97b95a1e2fa8ca1f4c30e | China | augmented language models | validation research | method | - |
| Conference Paper | Entity-Aware Image Caption Generation | Convolutional neural networks; Graph algorithms; Graphic methods; Inference engines; Natural language processing systems; Benchmark datasets; Collective inference; Effective approaches; Evaluation metrics; Image descriptions; Knowledge graphs; Short term memory; Specific information; Long short-term memory(...) | Current image captioning approaches generate descriptions which lack specific information, such as named entities that are involved in the images. In this paper we propose a new task which aims to generate informative image captions, given images and hashtags as input. We propose a simple but effective approach to tackle this problem. We first train a convolutional neural networks - long short term memory networks (CNN-LSTM) model to generate a template caption based on the input image. Then we (...) | ACL | 2020 | - | Lu D., Whitehead S., Huang L., Ji H., Chang S.-F. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067264580&partnerID=40&md5=c1864df7bbb9c6d1e6863614bad80a8c | United States | text generation | validation research | technique; resource | social media |
| Conference Paper | Evaluating the Impact of Knowledge Graph Context on Entity Disambiguation Models | context; knowledge graph; named entity disambiguation; pretrained transformers; roberta; wikidata; xlnet(...) | Pretrained Transformer models have emerged as state-of-the-art approaches that learn contextual information from the text to improve the performance of several NLP tasks. These models, albeit powerful, still require specialized knowledge in specific scenarios. In this paper, we argue that context derived from a knowledge graph (in our case: Wikidata) provides enough signals to inform pretrained transformer models and improve their performance for named entity disambiguation (NED) on Wikidata KG.(...) | Scopus | 2020 | 10.1145/3340531.3412159 | Mulang I.O., Singh K., Prabhu C., Nadgeri A., Hoffart J., Lehmann J. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095864148&doi=10.1145%2f3340531.3412159&partnerID=40&md5=0aae68a7daecda8963eaa1d41d366119 | Germany, India | entity linking | validation research | technique | - |
| Journal Article | Explainable Prediction of Medical Codes with Knowledge Graphs | automated ICD coding; explainable; knowledge graphs; medical records; natural language processing(...) | International Classification of Diseases (ICD) is an authoritative health care classification system of different diseases. It is widely used for disease and health records, assisted medical reimbursement decisions, and collecting morbidity and mortality statistics. The most existing ICD coding models only translate the simple diagnosis descriptions into ICD codes. And it obscures the reasons and details behind specific diagnoses. Besides, the label (code) distribution is uneven. And there is a (...) | Scopus | 2020 | 10.3389/fbioe.2020.00867 | Teng F., Yang W., Chen L., Huang L., Xu Q. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090009909&doi=10.3389%2ffbioe.2020.00867&partnerID=40&md5=787cd8bbbaa7baa2a50c0af6661edfa4 | China | text classification | validation research | method | health |
| Conference Paper | Exploiting Structured Knowledge in Text Via Graph-Guided Representation Learning | Benchmarking; Computational linguistics; Knowledge based systems; Learning systems; Entity-level; Knowledge graphs; Language model; Learn+; Masking schemes; Performance; Pre-training; Question Answering; Structured knowledge; Task learning; Knowledge graph(...) | In this work, we aim at equipping pre-trained language models with structured knowledge. We present two self-supervised tasks learning over raw text with the guidance from knowledge graphs. Building upon entity-level masked language models, our first contribution is an entity masking scheme that exploits relational knowledge underlying the text. This is fulfilled by using a linked knowledge graph to select informative entities and then masking their mentions. In addition, we use knowledge graphs(...) | ACL | 2020 | - | Shen T., Mao Y., He P., Long G., Trischler A., Chen W. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106164020&partnerID=40&md5=8e6384fcca0ff56f309431419ac32f17 | Australia, Canada | augmented language models | validation research | tool | - |
| Conference Paper | Exploring the Social Drivers of Health during a Pandemic: Leveraging Knowledge Graphs and Population Trends in Covid-19 | COVID-19 risk factors; Knowledge Graphs; Natural Language Processing; Population Trends; Relation Extraction; Social determinants of health(...) | Social determinants of health (SDoH) are the factors which lie outside of the traditional health system, such as employment or access to nutritious foods, that influence health outcomes. Some efforts have focused on identifying vulnerable populations during the COVID-19 pandemic, however, both the short-and long-term social impacts of the pandemic on individuals and populations are not well understood. This paper presents a pipeline to discover health outcomes and related social factors based on(...) | Scopus | 2020 | 10.3233/shti200684 | Bettencourt-Silva J.H., Mulligan N., Jochim C., Yadav N., Sedlazek W., Lopez V., Gleize M. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096733678&doi=10.3233%2fSHTI200684&partnerID=40&md5=f8281b9f3569922d2d958fdaead2bf79 | Ireland | entity extraction, relation extraction, semantic search | solution proposal | method | health |
| Conference Paper | Extracting and Representing Causal Knowledge of Health Conditions | AI; Causality; Health; Knowledge Graph; NLP(...) | Most healthcare and health research organizations published their health knowledge on the web through HTML or semantic presentations nowadays e.g. UK National Health Service website. Especially, the HTML contents contain valuable information about the individual health condition and graph knowledge presents the semantics of words in the contents. This paper focuses on combining these two for extracting causality knowledge. Understanding causality relations is one of the crucial tasks to support (...) | Scopus | 2020 | - | Yu H.Q. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098999851&partnerID=40&md5=8d540898a16e5de105e8f73c95b6c834 | United Kingdom | entity extraction, relation extraction, semantic search | solution proposal | method | health |
| Journal Article | Extraction of Information Related to Drug Safety Surveillance from Electronic Health Record Notes: Joint Modeling of Entities and Relations Using Knowledge-Aware Neural Attentive Models | Adverse drug events; Adverse drug reaction reporting systems; Deep learning; Electronic health records; Information extraction; Named entity recognition; Natural language processing; Relation extraction(...) | Background: An adverse drug event (ADE) is commonly defined as "an injury resulting from medical intervention related to a drug."Providing information related to ADEs and alerting caregivers at the point of care can reduce the risk of prescription and diagnostic errors and improve health outcomes. ADEs captured in structured data in electronic health records (EHRs) as either coded problems or allergies are often incomplete, leading to underreporting. Therefore, it is important to develop capabil(...) | Scopus | 2020 | 10.2196/18417 | Dandala B., Joopudi V., Tsou C.-H., Liang J.J., Suryanarayanan P. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097479168&doi=10.2196%2f18417&partnerID=40&md5=7ac69d2befaa5afb0d00b8fe9f2d7120 | United States | entity extraction, relation extraction, knowledge graph embedding | validation research | technique | health |
| Conference Paper | Faq-Based Question Answering Via Knowledge Anchors | Anchors; Knowledge representation; Query processing; Semantics; Effective solution; Frequently asked questions; Interpretability; Knowledge graphs; Matching models; Multi channel; Query documents; Question Answering; Natural language processing systems(...) | Question answering (QA) aims to understand questions and find appropriate answers. In real-world QA systems, Frequently Asked Question (FAQ) based QA is usually a practical and effective solution, especially for some complicated questions (e.g., How and Why). Recent years have witnessed the great successes of knowledge graphs (KGs) in KBQA systems, while there are still few works focusing on making full use of KGs in FAQ-based QA. In this paper, we propose a novel Knowledge Anchor based Question(...) | Scopus | 2020 | 10.1007/978-3-030-60450-9_1 | Xie R., Lu Y., Lin F., Lin L. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093109554&doi=10.1007%2f978-3-030-60450-9_1&partnerID=40&md5=a98121a3a05e1cb66b45614ed84e0d4f | China | question answering | validation research | method | - |
| Conference Paper | Fle at Clef Ehealth 2020: Text Mining and Semantic Knowledge for Automated Clinical Encoding | CLEF eHealth; Clinical encoding; Named entity recognition (NER); Semantic knowledge; Text mining(...) | In Healthcare domain, several documents are provided in a narrative way, following textual unstructured formats. This is the case of the discharge summaries, which are clinical texts where physicians describe the conditions of the patients with natural language, making the automated processing of such texts hard and challenging. The objective of the tasks of the 2020 CLEF eHealth for Multilingual Information Extraction is to develop solutions to automatically annotate Spanish clinical texts with(...) | Scopus | 2020 | - | García-Santa N., Cetina K. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113466369&partnerID=40&md5=f2a5e2151f16495bff6c43210d875892 | Spain | entity extraction, entity linking, semantic search | validation research | technique | health |
| Journal Article | Greg: a Global Level Relation Extraction with Knowledge Graph Embedding | Knowledge graph; Machine learning; Meta learning; Natural language processing; Relation extraction; Text summarization(...) | In an age overflowing with information, the task of converting unstructured data into structured data are a vital task of great need. Currently, most relation extraction modules are more focused on the extraction of local mention-level relations-usually from short volumes of text. However, in most cases, the most vital and important relations are those that are described in length and detail. In this research, we propose GREG: A Global level Relation Extractor model using knowledge graph embeddi(...) | Scopus | 2020 | 10.3390/app10031181 | Kim K., Hur Y., Kim G., Lim H. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081533252&doi=10.3390%2fapp10031181&partnerID=40&md5=a501a8e89d0ee5616b3f9462a8daade1 | South Korea | relation extraction, knowledge graph embedding | validation research | technique | - |
| Conference Paper | Harnessing Cross-Lingual Features to Improve Cognate Detection for Low-Resource Languages | - | Cognates are variants of the same lexical form across different languages; for example {}fonema{''} in Spanish and {}phoneme{''} in English are cognates, both of which mean {``}a unit of sound{''}. The task of automatic detection of cognates among any two languages can help downstream NLP tasks such as Cross-lingual Information Retrieval, Computational Phylogenetics, and Machine Translation. In this paper, we demonstrate the use of cross-lingual word embeddings for detecting cognates among f(...) |
ACL | 2020 | 10.18653/v1/2020.coling-main.119 | Kanojia, Diptesh and Dabre, Raj and Dewangan, Shubham and Bhattacharyya, Pushpak and Haffari, Gholamreza and Kulkarni, Malhar | https://aclanthology.org/2020.coling-main.119 | Australia, India, Japan | text classification | validation research | technique | - |
| Conference Paper | Hhh: an Online Medical Chatbot System Based on Knowledge Graph and Hierarchical Bi-Directional Attention | Hierarchial BiLSTM attention model; knowledge graph; medical chatbot.; natural language processing; question answering(...) | This paper proposes a chatbot framework that adopts a hybrid model which consists of a knowledge graph and a text similarity model. Based on this chatbot framework, we build HHH, an online question-and-answer (QA) Healthcare Helper system for answering complex medical questions. HHH maintains a knowledge graph constructed from medical data collected from the Internet. HHH also implements a novel text representation and similarity deep learning model, Hierarchical BiLSTM Attention Model (HBAM), t(...) | ACM | 2020 | 10.1145/3373017.3373049 | Bao Q., Ni L., Liu J. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079865624&doi=10.1145%2f3373017.3373049&partnerID=40&md5=03da275abf6df819631230ddce5ebf38 | New Zealand | question answering | validation research | tool; resource | health |
| Conference Paper | Incorporating Commonsense Knowledge Graph in Pretrained Models for Social Commonsense Tasks | - | Pretrained language models have excelled at many NLP tasks recently; however, their social intelligence is still unsatisfactory. To enable this, machines need to have a more general understanding of our complicated world and develop the ability to perform commonsense reasoning besides fitting the specific downstream tasks. External commonsense knowledge graphs (KGs), such as ConceptNet, provide rich information about words and their relationships. Thus, towards general commonsense learning, we p(...) | ACL | 2020 | 10.18653/v1/2020.deelio-1.9 | Chang, Ting-Yun and Liu, Yang and Gopalakrishnan, Karthik and Hedayatnia, Behnam and Zhou, Pei and Hakkani-Tur, Dilek | https://aclanthology.org/2020.deelio-1.9 | Taiwan, United States | augmented language models, natural language inference | validation research | technique | - |
| Conference Paper | Incorporating Domain Knowledge into Medical Nli Using Knowledge Graphs | Embeddings; Knowledge representation; Contextual words; Domain knowledge; Domain specific; Knowledge graphs; Medical domains; State of the art; State-of-the-art approach; Structured domain knowledge; Natural language processing systems(...) | Recently, biomedical version of embeddings obtained from language models such as BioELMo have shown state-of-the-art results for the textual inference task in the medical domain. In this paper, we explore how to incorporate structured domain knowledge, available in the form of a knowledge graph (UMLS), for the Medical NLI task. Specifically, we experiment with fusing embeddings obtained from knowledge graph with the state-of-the-art approaches for NLI task, which mainly rely on contextual word e(...) | ACL | 2020 | - | Sharma S., Santosh T.Y.S.S., Santra B., Ganguly N., Jana A., Goyal P. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084306601&partnerID=40&md5=15ad4243017b1c42293f5cfe7c28feb8 | India | augmented language models, natural language inference | validation research | tool | health |
| Conference Paper | Is Graph Structure Necessary for Multi-Hop Question Answering | - | Recently, attempting to model texts as graph structure and introducing graph neural networks to deal with it has become a trend in many NLP research areas. In this paper, we investigate whether the graph structure is necessary for textual multi-hop reasoning. Our analysis is centered on HotpotQA. We construct a strong baseline model to establish that, with the proper use of pre-trained models, graph structure may not be necessary for textual multi-hop reasoning. We point out that both graph stru(...) | ACL | 2020 | 10.18653/v1/2020.emnlp-main.583 | Shao, Nan and Cui, Yiming and Liu, Ting and Wang, Shijin and Hu, Guoping | https://aclanthology.org/2020.emnlp-main.583 | China | question answering | solution proposal | guidelines | - |
| Conference Paper | K-Bert: Enabling Language Representation with Knowledge Graph | Artificial intelligence; Domain knowledge; Domain specific; Domain-specific knowledge; Knowledge graphs; Knowledge incorporation; Loading models; Pre-training; Representation model; Knowledge representation(...) | Pre-trained language representation models, such as BERT, capture a general language representation from large-scale corpora, but lack domain-specific knowledge. When reading a domain text, experts make inferences with relevant knowledge. For machines to achieve this capability, we propose a knowledge-enabled language representation model (K-BERT) with knowledge graphs (KGs), in which triples are injected into the sentences as domain knowledge. However, too much knowledge incorporation may diver(...) | Scopus | 2020 | - | Liu W., Zhou P., Zhao Z., Wang Z., Ju Q., Deng H., Wang P. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106402604&partnerID=40&md5=f871a87e7bb104c4921c1497e39c8366 | China | augmented language models | validation research | tool | - |
| Conference Paper | Kbaa: an Adversarial Example Generation Method for Kbqa Task | adversarial example generation; KBQA task; Knowledge based adversarial attack; NLP model(...) | The adversarial example generation algorithm is currently a very popular algorithm for deceiving machine learning. The main method is to change the original sample in a way that is almost imperceptible to the user, and cause an obvious error in the result returned by the model. At present, there are many adversarial algorithms for computer vision, but there are few for NLP models, and there is almost no algorithm for Question Answer task. This paper designs a framework of adversarial example gen(...) | IEEE | 2020 | 10.1109/dsa51864.2020.00056 | Guo S., Wang S., Liu B., Shi T. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100577390&doi=10.1109%2fDSA51864.2020.00056&partnerID=40&md5=69daa994567a2ecaaa3f213d97fc9def | China | question generation, question answering | solution proposal | technique | - |
| Journal Article | Kgen: a Knowledge Graph Generator from Biomedical Scientific Literature | Information Extraction; Knowledge Graphs; Ontologies; RDF Triples(...) | Background: Knowledge is often produced from data generated in scientific investigations. An ever-growing number of scientific studies in several domains result into a massive amount of data, from which obtaining new knowledge requires computational help. For example, Alzheimer’s Disease, a life-threatening degenerative disease that is not yet curable. As the scientific community strives to better understand it and find a cure, great amounts of data have been generated, and new knowledge can be (...) | Scopus | 2020 | 10.1186/s12911-020-01341-5 | Rossanez A., dos Reis J.C., Torres R.S., de Ribaupierre H. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097554226&doi=10.1186%2fs12911-020-01341-5&partnerID=40&md5=fc9c69ff8b1fb180e8895d0c46348969 | Brazil, United Kingdom, Norway | entity extraction, relation extraction, ontology construction | validation research | tool; resource | health |
| Conference Paper | Knowledge Aware Conversation Generation with Explainable Reasoning over Augmented Graphs | Flow graphs; Graph algorithms; Knowledge representation; Augmented graph; Knowledge graphs; Reading comprehension; Reasoning algorithms; Response generation; Selection decisions; State of the art; Text information; Natural language processing systems(...) | Two types of knowledge, triples from knowledge graphs and texts from documents, have been studied for knowledge aware open-domain conversation generation, in which graph paths can narrow down vertex candidates for knowledge selection decision, and texts can provide rich information for response generation. Fusion of a knowledge graph and texts might yield mutually reinforcing advantages, but there is less study on that. To address this challenge, we propose a knowledge aware chatting machine wit(...) | ACL | 2020 | - | Liu Z., Niu Z.-Y., Wu H., Wang H. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084297175&partnerID=40&md5=7642d4a3a608d25d0744108da05492a3 | China | conversational interfaces, question answering | validation research | tool | - |
| Conference Paper | Knowledge Detection and Discovery Using Semantic Graph Embeddings on Large Knowledge Graphs Generated on Text Mining Results | Clinical research; Data integration; Decision making; Digital storage; Embeddings; Graphic methods; Information retrieval; Information systems; Information use; Knowledge representation; Natural language processing systems; Semantics; Algorithmic approach; Clinical decision making; Context information; Document Clustering; Knowledge extraction; Language technology; Scientific literature; Unstructured texts; Text mining(...) | Knowledge graphs play a central role in big data integration, especially for connecting data from different domains. Bringing unstructured texts, e.g. from scientific literature, into a structured, comparable format is one of the key assets. Here, we use knowledge graphs in the biomedical domain working together with text mining based document data for knowledge extraction and retrieval from text and natural language structures. For example cause and effect models, can potentially facilitate cli(...) | Scopus | 2020 | 10.15439/2020f36 | Dorpinghaus J., Jacobs M. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095789650&doi=10.15439%2f2020F36&partnerID=40&md5=eec13e5800bbfd9a26200cff2d73104e | Germany | knowledge graph embedding, semantic search | validation research | method | health |
| Conference Paper | Knowledge Graph Construction for Intelligent Analysis of Social Networking User Opinion | Sentiment analysis; Natural language processing; Knowledge graph; User opinion(...) | Microblogging is a popular social networking tool on which people tend to express their views and opinions. As such, the massive data on microblogging platforms mean abundant research value to social science researchers. To help them better analyze these data, a framework for understanding diverse user opinions and identifying complex relationships in the form of knowledge graphs is proposed in this paper. The two main tasks in the framework are sentiment analysis and knowledge graph constructio(...) | WoS | 2020 | 10.1007/978-3-030-34986-8_17 | Xie T,Yang Y,Li Q,Liu X,Wang H | http://dx.doi.org/10.1007/978-3-030-34986-8_17 | China | text analysis, relation extraction | solution proposal | method | social media |
| Conference Paper | Knowledge Graph Construction of Personal Relationships | Entity alignment; Entity recognition; Knowledge graph; Personal relationships; Relation extraction(...) | Knowledge graph has attracted much attention in recent years. It is a high-level natural language processing (NLP) problem, which includes many NLP tasks such as named entity recognition, relation extraction, entity alignment, etc. In this paper, we focus on the entity of persons in the large amount of text data, and then construct the graph of personal relationships. Firstly we investigate how to recognize person names from Chinese text. Secondly, we propose a comprehensive approach including I(...) | Scopus | 2020 | 10.1007/978-3-030-57884-8_40 | Jin Y., Jin Q., Yang X. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091296764&doi=10.1007%2f978-3-030-57884-8_40&partnerID=40&md5=e51d60a77294ca26e3ed675dd07b40bb | China | entity extraction, relation extraction | solution proposal | method | - |
| Conference Paper | Knowledge Graph Enhanced Event Extraction in Financial Documents | Event Extraction; Financial Documents; Financial Events; Graph Neural Network; Knowledge Graph(...) | Event extraction is a classic task in natural language processing with wide use in handling large amount of yet rapidly growing financial, legal, medical, and government documents which often contain multiple events with their elements scattered and mixed across the documents, making the problem much more difficult. Though the underlying relations between event elements to be extracted provide helpful contextual information, they are somehow overlooked in prior studies.We showcase the enhancemen(...) | IEEE | 2020 | 10.1109/bigdata50022.2020.9378471 | Guo K., Jiang T., Zhang H. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103832676&doi=10.1109%2fBigData50022.2020.9378471&partnerID=40&md5=05210f4c3c232f2d219d9fdfde8b04ed | China | entity extraction, relation extraction | validation research | technique | business |
| Journal Article | Knowledge Graphs Effectiveness in Neural Machine Translation Improvement | knowledge graph representation; natural language processing; neural machine translation(...) | Maintaining semantic relations between words during the translation process yields more accurate target-language output from Neural Machine Translation (NMT). Although difficult to achieve from training data alone, it is possible to leverage Knowledge Graphs (KGs) to retain source-language semantic relations in the corresponding target-language translation. The core idea is to use KG entity relations as embedding constraints to improve the mapping from source to target. This paper describes two (...) | Scopus | 2020 | 10.7494/csci.2020.21.3.3701 | Ahmadnia B., Dorr B.J., Kordjamshidi P. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094217000&doi=10.7494%2fcsci.2020.21.3.3701&partnerID=40&md5=c263094a1fc34615f56ad875ee9a86fa | United States | machine translation | validation research | technique | - |
| Journal Article | Knowledge-Driven Joint Posterior Revision of Named Entity Classification and Linking | - | In this work we address the problem of extracting quality entity knowledge from natural language text, an important task for the automatic construction of knowledge graphs from unstructured content. More in details, we investigate the benefit of performing a joint posterior revision, driven by ontological background knowledge, of the annotations resulting from natural language processing (NLP) entity analyses such as named entity recognition and classification (NERC) and entity linking (EL). The(...) | ScienceDirect | 2020 | 10.1016/j.websem.2020.100617 | Marco Rospocher and Francesco Corcoglioniti | https://www.sciencedirect.com/science/article/pii/S1570826820300500 | Italy | error detection, entity linking, entity classification | validation research | technique | - |
| Conference Paper | Knowledge-Enhanced Natural Language Inference Based on Knowledge Graphs | - | Natural Language Inference (NLI) is a vital task in natural language processing. It aims to identify the logical relationship between two sentences. Most of the existing approaches make such inference based on semantic knowledge obtained through training corpus. The adoption of background knowledge is rarely seen or limited to a few specific types. In this paper, we propose a novel Knowledge Graph-enhanced NLI (KGNLI) model to leverage the usage of background knowledge stored in knowledge graphs(...) | ACL | 2020 | 10.18653/v1/2020.coling-main.571 | Wang, Zikang and Li, Linjing and Zeng, Daniel | https://aclanthology.org/2020.coling-main.571 | China | augmented language models, knowledge graph embedding, natural language inference | validation research | technique | - |
| Conference Paper | Knowledge-Guided Open Attribute Value Extraction with Reinforcement Learning | Computational linguistics; Natural language processing systems; Reinforcement learning; Attribute values; Extraction accuracy; Information extraction systems; Knowledge graphs; Question Answering Task; Updated informations; Web Corpora; Knowledge graph(...) | Open attribute value extraction for emerging entities is an important but challenging task. A lot of previous works formulate the problem as a question-answering (QA) task. While the collections of articles from web corpus provide updated information about the emerging entities, the retrieved texts can be noisy, irrelevant, thus leading to inaccurate answers. Effectively filtering out noisy articles as well as bad answers is the key to improving extraction accuracy. Knowledge graph (KG), which c(...) | ACL | 2020 | - | Liu Y., Zhang S., Song R., Feng S., Xiao Y. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118453193&partnerID=40&md5=784222ec90b38b7904c3cce7994ec32e | China, United States | attribute extraction | validation research | technique | - |
| Conference Paper | Knowlybert - Hybrid Query Answering over Language Models and Knowledge Graphs | Knowledge graphs; Language models; Query answering(...) | Providing a plethora of entity-centric information, Knowledge Graphs have become a vital building block for a variety of intelligent applications. Indeed, modern knowledge graphs like Wikidata already capture several billions of RDF triples, yet they still lack a good coverage for most relations. On the other hand, recent developments in NLP research show that neural language models can easily be queried for relational knowledge without requiring massive amounts of training data. In this work, w(...) | Scopus | 2020 | 10.1007/978-3-030-62419-4_17 | Kalo J.-C., Fichtel L., Ehler P., Balke W.-T. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096523582&doi=10.1007%2f978-3-030-62419-4_17&partnerID=40&md5=298df314572f6ae44a99122047f6bfa2 | Germany | question answering, augmented language models | validation research | tool | - |
| Conference Paper | Kore 50Dywc: an Evaluation Data Set for Entity Linking Based on Dbpedia, Yago, Wikidata, and Crunchbase | Data Sets; Entity Linking; Knowledge Graph; NLP Interchange Format; Text Annotation(...) | A major domain of research in natural language processing is named entity recognition and disambiguation (NERD). One of the main ways of attempting to achieve this goal is through use of Semantic Web technologies and its structured data formats. Due to the nature of structured data, information can be extracted more easily, therewith allowing for the creation of knowledge graphs. In order to properly evaluate a NERD system, gold standard data sets are required. A plethora of different evaluation(...) | Scopus | 2020 | - | Noullet K., Mix R., Färber M. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096516371&partnerID=40&md5=677a5dad7c59d18227b3ffb1ff95bc2a | Germany | entity linking | validation research | resource | - |
| Conference Paper | Kore 50^Dywc: an Evaluation Data Set for Entity Linking Based on Dbpedia, Yago, Wikidata, and Crunchbase | - | A major domain of research in natural language processing is named entity recognition and disambiguation (NERD). One of the main ways of attempting to achieve this goal is through use of Semantic Web technologies and its structured data formats. Due to the nature of structured data, information can be extracted more easily, therewith allowing for the creation of knowledge graphs. In order to properly evaluate a NERD system, gold standard data sets are required. A plethora of different evaluation(...) | ACL | 2020 | - | Noullet, Kristian and Mix, Rico and F{"a}rber, Michael | https://aclanthology.org/2020.lrec-1.291 | Germany | entity linking, text analysis | validation research | resource | - |
| Conference Paper | Label-Free Distant Supervision for Relation Extraction Via Knowledge Graph Embedding | Embeddings; Extraction; Labeled data; Natural language processing systems; Knowledge graphs; Label free; Prior knowledge; Relation extraction; Type information; Data mining(...) | Distant supervision is an effective method to generate large scale labeled data for relation extraction, which assumes that if a pair of entities appears in some relation of a Knowledge Graph (KG), all sentences containing those entities in a large unlabeled corpus are then labeled with that relation to train a relation classifier. However, when the pair of entities has multiple relationships in the KG, this assumption may produce noisy relation labels. This paper proposes a label-free distant s(...) | ACL | 2020 | - | Wang G., Zhang W., Wang R., Zhou Y., Chen X., Zhang W., Zhu H., Chen H. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081738408&partnerID=40&md5=a42733c5b20e34263c5f089f881fbb43 | China | relation extraction, knowledge graph embedding | validation research | technique | - |
| Conference Paper | Language Generation with Multi-Hop Reasoning on Commonsense Knowledge Graph | Computational linguistics; Flow graphs; Semantics; Commonsense knowledge; Knowledge graphs; Language generation; Language model; Multi-hops; Semantics Information; Structural information; Text generations; Knowledge graph(...) | Despite the success of generative pre-trained language models on a series of text generation tasks, they still suffer in cases where reasoning over underlying commonsense knowledge is required during generation. Existing approaches that integrate commonsense knowledge into generative pre-trained language models simply transfer relational knowledge by post-training on individual knowledge triples while ignoring rich connections within the knowledge graph. We argue that exploiting both the structu(...) | ACL | 2020 | - | Ji H., Ke P., Huang S., Wei F., Zhu X., Huang M. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098817089&partnerID=40&md5=80402872e9e00ba5fec13b8a9d624528 | China | augmented language models, text generation | validation research | tool | - |
| Conference Paper | Latent Relation Language Models | Computational linguistics; Knowledge representation; In contexts; Joint distributions; Knowledge graphs; Language model; Posterior probability; Qualitative analysis; Modeling languages(...) | In this paper, we propose Latent Relation Language Models (LRLMs), a class of language models that parameterizes the joint distribution over the words in a document and the entities that occur therein via knowledge graph relations. This model has a number of attractive properties: it not only improves language modeling performance, but is also able to annotate the posterior probability of entity spans for a given text through relations. Experiments demonstrate empirical improvements over both wo(...) | Scopus | 2020 | - | Hayashi H., Hu Z., Xiong C., Neubig G. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095149010&partnerID=40&md5=0136dc85f139a9219c38f6db0fa02f90 | United States | relation extraction, augmented language models | validation research | tool | - |
| Conference Paper | Learning Conceptual-Contextual Embeddings for Medical Text | Benchmarking; Encoding (symbols); Knowledge representation; Semantics; Text processing; Context modeling; Electronic health record (EHRs); External knowledge; Medical text processing; Natural language understanding; Structured knowledge; Text representation; Text representation models; Embeddings(...) | External knowledge is often useful for natural language understanding tasks. We introduce a contextual text representation model called Conceptual-Contextual (CC) embeddings, which incorporates structured knowledge into text representations. Unlike entity embedding methods, our approach encodes a knowledge graph into a context model. CC embeddings can be easily reused for a wide range of tasks in a similar fashion to pre-trained language models. Our model effectively encodes the huge UMLS databa(...) | Scopus | 2020 | - | Zhang X., Dou D., Wu J. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097815289&partnerID=40&md5=48c8c9d3dacae571301bcb2a6e15d214 | China, United States | augmented language models | validation research | technique | health |
| Conference Paper | Legal Knowledge Extraction for Knowledge Graph Based Question-Answering | Legal Knowledge Extraction; Ontology Design Pattern Alignment; Question-Answering(...) | This paper presents the Open Knowledge Extraction (OKE) tools combined with natural language analysis of the sentence in order to enrich the semantic of the legal knowledge extracted from legal text. In particular the use case is on international private law with specific regard to the Rome I Regulation EC 593/2008, Rome II Regulation EC 864/2007, and Brussels I bis Regulation EU 1215/2012. A Knowledge Graph (KG) is built using OKE and Natural Language Processing (NLP) methods jointly with the m(...) | Scopus | 2020 | 10.3233/faia200858 | Sovrano F., Palmirani M., Vitali F. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098663436&doi=10.3233%2fFAIA200858&partnerID=40&md5=3225d78234530e3dbe9397f72d351338 | Italy | entity extraction, relation extraction, ontology construction, question answering | solution proposal | tool; resource | law |
| Conference Paper | Link Prediction Using Semi-Automated Ontology and Knowledge Graph in Medical Sphere | COVID-19; Deep learning; Graph convolutional networks; Knowledge Graph; link prediction; MeSH; Natural language processing; Ontology(...) | Presently, medical professionals and researchers face a dire problem trying to identify important and subject specific documents for medical research. This is mainly owing to the fact that there is a disconnection in the pipeline for finding essential documents via a common platform which can parse and link the complex medical terminologies. To solve this problem, a model is generated, which creates a Semi-automated ontology and Knowledge-graph for link prediction using unstructured medical docu(...) | IEEE | 2020 | 10.1109/indicon49873.2020.9342301 | Varma S., Shivam S., Jamaiyar R., Anukriti A., Kashyap S., Sarkar A. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101501532&doi=10.1109%2fINDICON49873.2020.9342301&partnerID=40&md5=934e555733cacf01164cde61e33f5ac5 | India | link prediction, entity extraction, ontology construction | solution proposal | technique | health |
| Conference Paper | Machine Reading Comprehension Using Structural Knowledge Graph-Aware Network | Knowledge management; Knowledge representation; Comprehension tasks; Emerging trends; External knowledge; Knowledge graphs; Reading comprehension; State-of-the-art performance; Structural information; Structural knowledge; Natural language processing systems(...) | Leveraging external knowledge is an emerging trend in machine comprehension task. Previous work usually utilizes knowledge graphs such as ConceptNet as external knowledge, and extracts triples from them to enhance the initial representation of the machine comprehension context. However, such method cannot capture the structural information in the knowledge graph. To this end, we propose a Structural Knowledge Graph-aware Network (SKG) model, constructing sub-graphs for entities in the machine co(...) | ACL | 2020 | - | Qiu D., Zhang Y., Feng X., Liao X., Jiang W., Lyu Y., Liu K., Zhao J. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084322390&partnerID=40&md5=6f8608bee6e91a2c1f68f09829f92b3b | China | question answering | validation research | technique | - |
| Conference Paper | Mapping Text to Knowledge Graph Entities Using Multi-Sense Lstms | Knowledge based systems; Long short-term memory; Mapping; Semantics; Text processing; Classification tasks; Compositional modeling; Knowledge graphs; Mapping process; Multi-dimensional entities; Natural language text; State of the art; Textual features; Natural language processing systems(...) | This paper addresses the problem of mapping natural language text to knowledge base entities. The mapping process is approached as a composition of a phrase or a sentence into a point in a multi-dimensional entity space obtained from a knowledge graph. The compositional model is an LSTM equipped with a dynamic disambiguation mechanism on the input word embeddings (a Multi-Sense LSTM), addressing polysemy issues. Further, the knowledge base space is prepared by collecting random walks from a grap(...) | ACL | 2020 | - | Kartsaklis D., Pilehvar M.T., Collier N. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081720002&partnerID=40&md5=76e164a9517a73ff7c470458a711c14c | United Kingdom | entity linking, text classification, entity classification | validation research | technique | - |
| Conference Paper | Measuring Semantic Similarity Across Eu Gdpr Regulation and Cloud Privacy Policies | Big Data Categories; Document Similarity; General Data Protection Regulation; Ontology; Organizations; Semantic Web; Text Extraction(...) | Data protection authorities formulate policies and rules which the service providers have to comply with to ensure security and privacy when they perform Big Data analytics using users Personally Identifiable Information (PII). The knowledge contained in the data regulations and organizational privacy policies are typically maintained as short unstructured text in HTML or PDF formats. Hence it is an open challenge to determine the specific regulation rules that are being addressed by a provider'(...) | IEEE | 2020 | 10.1109/bigdata50022.2020.9377864 | Elluri L., Pande Joshi K., Kotal A. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103823587&doi=10.1109%2fBigData50022.2020.9377864&partnerID=40&md5=57463a0ba8d61a88b87ff148a3314eda | United States | semantic similarity | solution proposal | method | law |
| Journal Article | Microsoft Academic Graph: When Experts Are Not Enough | Citation networks; Eigenvector centrality measure; Knowledge graph; Research assessments; Saliency ranking; Scholarly database(...) | An ongoing project explores the extent to which artificial intelligence (AI), specifically in the areas of natural language processing and semantic reasoning, can be exploited to facilitate the studies of science by deploying software agents equipped with natural language understanding capabilities to read scholarly publications on the web. The knowledge extracted by these AI agents is organized into a heterogeneous graph, called Microsoft Academic Graph (MAG), where the nodes and the edges repr(...) | Scopus | 2020 | 10.1162/qss_a_00021 | Wang K., Shen Z., Huang C., Wu C.-H., Dong Y., Kanakia A. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090098906&doi=10.1162%2fqss_a_00021&partnerID=40&md5=09041be5f5714e2b799f073144d2720b | United States | semantic search | solution proposal | method; resource | scholarly domain |
| Journal Article | Mining Temporal Evolution of Knowledge Graphs and Genealogical Features for Literature-Based Discovery Prediction | Dynamic Supervised Link Prediction; Genealogical Community; Keyword Co-occurrence Network (KCN); Literature-based Knowledge Discovery; Weighted Temporal Citation(...) | Literature-based discovery process identifies the important but implicit relations among information embedded in published literature. Existing techniques from Information Retrieval (IR) and Natural Language Processing (NLP) attempt to identify the hidden or unpublished connections between information concepts within published literature, however, these techniques overlooked the concept of predicting the future and emerging relations among scientific knowledge components such as author selected (...) | ScienceDirect | 2020 | 10.1016/j.joi.2020.101057 | Choudhury N., Faisal F., Khushi M. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092253994&doi=10.1016%2fj.joi.2020.101057&partnerID=40&md5=3640be71a2e6570f1b3911b7c3b3f288 | Australia, United States | link prediction, semantic search | validation research | method | history |
| Journal Article | Mining the Sociome for Health Informatics: Analysis of Therapeutic Lifestyle Adherence of Diabetic Patients in Twitter | Sociome, Community detection, Topic modelling, Knowledge graphs, Diabetes, Twitter(...) | In recent years, the number of active users in social media has grown exponentially. Despite the thematic diversity of the messages, social media have become an important vehicle to disseminate health information as well as to gather insights about patients’ experiences and emotional intelligence. Therefore, the present work proposes a new methodology of analysis to identify and interpret the behaviour, perceptions and appreciations of patients and close relatives towards a health condition thro(...) | ScienceDirect | 2020 | 10.1016/j.future.2020.04.025 | Gael Pérez-Rodríguez and Martín Pérez-Pérez and Florentino Fdez-Riverola and Anália Lourenço | https://www.sciencedirect.com/science/article/pii/S0167739X19329516 | Spain, Portugal | entity extraction, relation extraction | solution proposal | method | health; social media |
| Conference Paper | Motoria: Automatic E-Learning Course Generation System | Automatic course generation systems; e-learning course content; graph; Machine Learning; natural language processing(...) | Recently, through the availability of open data sources and the existence of advanced techniques of Natural Language Processing (NLP) and Artificial Intelligence, several tools have emerged to support the educational learning process of students and lecturers (experts in different knowledge domains). However, there are very few works which propose tools that automate the e-learning course content development process. Hence, experts in the field of educational training have shown great interest i(...) | ACM | 2020 | 10.1145/3397125.3397128 | Del Carmen Rodríguez-Hernández M., De La Vega Rodrigálvarez-Chamarro M., Vea-Murguia Merck J.I., Ballano Á.E., Lafuente M.A., Del Hoyo-Alonso R. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086182575&doi=10.1145%2f3397125.3397128&partnerID=40&md5=48d39603fe19331b3ad5dccc9614db1e | Spain | entity extraction, relation extraction, semantic search | validation research | tool | education |
| Conference Paper | Multi-Hop Knowledge Graph Reasoning with Reward Shaping | Embeddings; Reinforcement learning; Action sequences; Benchmark datasets; Effective approaches; False negatives; Incomplete knowledge; Knowledge graphs; Low qualities; Query answering; Natural language processing systems(...) | Multi-hop reasoning is an effective approach for query answering (QA) over incomplete knowledge graphs (KGs). The problem can be formulated in a reinforcement learning (RL) setup, where a policy-based agent sequentially extends its inference path until it reaches a target. However, in an incomplete KG environment, the agent receives low-quality rewards corrupted by false negatives in the training data, which harms generalization at test time. Furthermore, since no golden action sequence is used (...) | ACL | 2020 | - | Lin X.V., Socher R., Xiong C. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081715825&partnerID=40&md5=e1a964431bd28a2432bd7b51c780140a | - | question answering, knowledge graph embedding | validation research | technique | - |
| Conference Paper | Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction | Information use; Coreference; Domain specific; Multi-task model; Scientific articles; Scientific information; Scientific knowledge; Scientific literature; Unified framework; Natural language processing systems(...) | We introduce a multi-task setup of identifying and classifying entities, relations, and coreference clusters in scientific articles. We create SCIERC, a dataset that includes annotations for all three tasks and develop a unified framework called Scientific Information Extractor (SCIIE) for with shared span representations. The multi-task setup reduces cascading errors between tasks and leverages cross-sentence relations through coreference links. Experiments show that our multi-task model outper(...) | ACL | 2020 | - | Luan Y., He L., Ostendorf M., Hajishirzi H. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081754181&partnerID=40&md5=709a9ea70a6f4e7a6d4b10acc623c89a | United States | entity extraction, relation extraction, text analysis | validation research | tool; resource | - |
| Conference Paper | Multi-Task Learning for Knowledge Graph Completion with Pre-Trained Language Models | - | As research on utilizing human knowledge in natural language processing has attracted considerable attention in recent years, knowledge graph (KG) completion has come into the spotlight. Recently, a new knowledge graph completion method using a pre-trained language model, such as KG-BERT, is presented and showed high performance. However, its scores in ranking metrics such as Hits@k are still behind state-of-the-art models. We claim that there are two main reasons: 1) failure in sufficiently lea(...) | ACL | 2020 | 10.18653/v1/2020.coling-main.153 | Kim, Bosung and Hong, Taesuk and Ko, Youngjoong and Seo, Jungyun | https://aclanthology.org/2020.coling-main.153 | South Korea | link prediction, relation classification | validation research | method | - |
| Conference Paper | Multiple Knowledge Graphdb (Mkgdb) | - | We present MKGDB, a large-scale graph database created as a combination of multiple taxonomy backbones extracted from 5 existing knowledge graphs, namely: ConceptNet, DBpedia, WebIsAGraph, WordNet and the Wikipedia category hierarchy. MKGDB, thanks the versatility of the Neo4j graph database manager technology, is intended to favour and help the development of open-domain natural language processing applications relying on knowledge bases, such as information extraction, hypernymy discovery, top(...) | ACL | 2020 | - | Faralli, Stefano and Velardi, Paola and Yusifli, Farid | https://aclanthology.org/2020.lrec-1.283 | Italy | entity alignment | validation research | method; resource | - |
| Conference Paper | Neural Compositional Denotational Semantics for Question Answering | Gradient methods; Semantics; Syntactics; Trees (mathematics); Composition functions; Composition operators; Denotational semantics; Gradient descent; Knowledge graphs; Question Answering; Semantic operators; Syntactic structure; Natural language processing systems(...) | Answering compositional questions requiring multi-step reasoning is challenging. We introduce an end-to-end differentiable model for interpreting questions about a knowledge graph (KG), which is inspired by formal approaches to semantics. Each span of text is represented by a denotation in a KG and a vector that captures ungrounded aspects of meaning. Learned composition modules recursively combine constituent spans, culminating in a grounding for the complete sentence which answers the question(...) | ACL | 2020 | - | Gupta N., Lewis M. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081749991&partnerID=40&md5=6e1825813feae6e068d1eeff5f3b7153 | United States | question answering | validation research | technique | - |
| Conference Paper | Neural Machine Translation for Semantic-Driven Q&A Systems in the Factory Planning | answering models; artificial neural networks; factory planning; knowledge graph; natural language processing; question; semantic web stack(...) | Shorter lifecycles, increasing product variance and the integration of new products and technologies into existing factories lead to a high complexity in today's factory planning. In order to master this complexity, many companies attempt to improve their processes by using digitalization tools. This generates enormous amounts of data, which are currently only partially managed centrally in the company. In order to simplify the associated difficulties regarding the access to information, semanti(...) | ScienceDirect | 2020 | 10.1016/j.procir.2021.01.044 | Dombrowski U., Reiswich A., Lamprecht R. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101100470&doi=10.1016%2fj.procir.2021.01.044&partnerID=40&md5=e9654a429796d1777da8b401d8a6b0a8 | Germany | question answering | validation research | method | engineering |
| Conference Paper | Nlpcontributions: an Annotation Scheme for Machine Reading of Scholarly Contributions in Natural Language Processing Literature | Annotation guidelines; Dataset; Digital libraries; Open science graphs; Scholarly knowledge graphs; Semantic publishing(...) | We describe an annotation initiative to capture the scholarly contributions in natural language processing (NLP) articles, particularly, for the articles that discuss machine learning (ML) approaches for various information extraction tasks. We develop the annotation task based on a pilot annotation exercise on 50 NLP-ML scholarly articles presenting contributions to five information extraction tasks 1. machine translation, 2. named entity recognition, 3. question answering, 4. relation classifi(...) | Scopus | 2020 | - | D'Souza J., Auer S. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090918844&partnerID=40&md5=9cb6e55488d818c56b7307c5f2e45c37 | Germany | machine translation, question answering, relation classification, text classification | solution proposal | resource; guidelines | scholarly domain |
| Conference Paper | Nmt Enhancement Based on Knowledge Graph Mining with Pre-Trained Language Model | Knowledge Graph; NMT; Pre-trained Language Model(...) | Pre-trained language models like Bert, RoBERTa, GPT, etc. have achieved SOTA effects on multiple NLP tasks (e.g. sentiment classification, information extraction, event extraction, etc.). We propose a simple method based on knowledge graph to improve the quality of machine translation. First, we propose a multi-task learning model that learns subjects, objects, and predicates at the same time. Second, we treat different predicates as different fields, and improve the recognition ability of NMT m(...) | IEEE | 2020 | 10.23919/icact48636.2020.9061292 | Yang H., Qin Y., Deng Y., Wang M. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083976347&doi=10.23919%2fICACT48636.2020.9061292&partnerID=40&md5=cc951d3c317a2158323411691da1d742 | China | machine translation, augmented language models | validation research | technique | - |
| Conference Paper | On the Utilization of Structural and Textual Information of a Scientific Knowledge Graph to Discover Future Research Collaborations: a Link Prediction Perspective | Document representation; Future research collaborations; Link prediction; Natural language processing; Research knowledge graphs(...) | We consider the discovery of future research collaborations as a link prediction problem applied on scientific knowledge graphs. Our approach integrates into a single knowledge graph both structured and unstructured textual data through a novel representation of multiple scientific documents. The Neo4j graph database is used for the representation of the proposed scientific knowledge graph. For the implementation of our approach, we use the Python programming language and the scikit-learn ML lib(...) | Scopus | 2020 | 10.1007/978-3-030-61527-7_29 | Giarelis N., Kanakaris N., Karacapilidis N. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094142033&doi=10.1007%2f978-3-030-61527-7_29&partnerID=40&md5=1c7c2e382f4f25258c14c99ddf4f90f3 | Greece | link prediction | validation research | technique | scholarly domain |
| Conference Paper | Open Domain Question Answering Based on Text Enhanced Knowledge Graph with Hyperedge Infusion | Computational linguistics; Convolution; Convolutional neural networks; Semantics; Convolutional networks; Hyper graph; Hyperedges; Incomplete knowledge; Knowledge graphs; Open domain question answering; Performance; Question Answering; Semantics Information; Text information; Knowledge based systems(...) | The incompleteness of knowledge base (KB) is a vital factor limiting the performance of question answering (QA). This paper proposes a novel QA method by leveraging text information to enhance the incomplete KB. The model enriches the entity representation through semantic information contained in the text, and employs graph convolutional networks to update the entity status. Furthermore, to exploit the latent structural information of text, we treat the text as hyperedges connecting entities am(...) | ACL | 2020 | - | Han J., Cheng B., Wang X. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103277164&partnerID=40&md5=886b09d592842d3691336ab2d1cbd5fc | China | question answering | validation research | technique | - |
| Conference Paper | Opendialkg: Explainable Conversational Reasoning with Attention-Based Walks over Knowledge Graphs | Fact knowledge; Human evaluation; Knowledge graphs; Parallel corpora; Reasoning models; Rule-based models; State of the art; Walker models; Computational linguistics(...) | We study a conversational reasoning model that strategically traverses through a large-scale common fact knowledge graph (KG) to introduce engaging and contextually diverse entities and attributes. For this study, we collect a new Open-ended Dialog ? KG parallel corpus called OpenDialKG, where each utterance from 15K human-to-human role-playing dialogs is manually annotated with ground-truth reference to corresponding entities and paths from a large-scale KG with 1M+ facts. We then propose the D(...) | ACL | 2020 | - | Moon S., Shah P., Kumar A., Subba R. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082383935&partnerID=40&md5=6d03b54489b80a0eca5f0a51d5300161 | - | conversational interfaces | validation research | resource; technique | - |
| Conference Paper | Orchestrating Nlp Services for the Legal Domain | Applications; Knowledge Discovery/Representation; Systems; Text Analytics; Tools(...) | Legal technology is currently receiving a lot of attention from various angles. In this contribution we describe the main technical components of a system that is currently under development in the European innovation project Lynx, which includes partners from industry and research. The key contribution of this paper is a workflow manager that enables the flexible orchestration of workflows based on a portfolio of Natural Language Processing and Content Curation services as well as a Multilingua(...) | ACL | 2020 | - | Moreno-Schneider J., Rehm G., Montiel-Ponsoda E., Rodríguez-Doncel V., Revenko A., Karampatakis S., Khvalchik M., Sageder C., Gracia J., Maganza F. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094515387&partnerID=40&md5=33cbdc2f68cab07c3c4f581ca3d2edf9 | Austria, Germany, Spain, Italy | entity extraction, relation extraction, semantic search | solution proposal | tool | law |
| Conference Paper | Pathqg: Neural Question Generation from Facts | Computational linguistics; Query processing; End to end; Human evaluation; Knowledge graphs; Novel task; Performance; Query paths; Query representations; Sequence Labeling; State-of-the-art approach; Variational framework; Knowledge graph(...) | Existing research for question generation encodes the input text as a sequence of tokens without explicitly modeling fact information. These models tend to generate irrelevant and uninformative questions. In this paper, we explore to incorporate facts in the text for question generation in a comprehensive way. We present a novel task of question generation given a query path in the knowledge graph constructed from the input text. We divide the task into two steps, namely, query representation le(...) | ACL | 2020 | - | Wang S., Wei Z., Fan Z., Huang Z., Sun W., Zhang Q., Huang X. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109540289&partnerID=40&md5=0858e6c6c56ce1069bcc3c7d8ce02c56 | China | question answering, question generation | validation research | technique | - |
| Journal Article | Person-Relation Extraction Using Bert Based Knowledge Graph | Knowledge graph; Named entity recognition; Relation extraction(...) | Artificial intelligence technology has been actively researched in the areas of image processing and natural language processing. Recently, with the release of Google’s language model BERT, the importance of artificial intelligence models has attracted attention in the field of natural language processing. In this paper, we propose a knowledge graph to build a model that can extract people in a document using BERT, and to grasp the relationship between people based on the model. In addition, to (...) | Scopus | 2020 | 10.24507/icicelb.11.06.539 | Yang S.M., Yoo S.Y., Ahn Y.S., Jeong O.R. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084368302&doi=10.24507%2ficicelb.11.06.539&partnerID=40&md5=8a7e5c7967001245abbdb062c2ed5ea0 | South Korea | entity extraction, relation extraction | validation research | technique | - |
| Journal Article | Petrokg: Construction and Application of Knowledge Graph in Upstream Area of Petrochina | knowledge graph; natural language processing; oil and gas industry(...) | There is a large amount of heterogeneous data distributed in various sources in the upstream of PetroChina. These data can be valuable assets if we can fully use them. Meanwhile, the knowledge graph, as a new emerging technique, provides a way to integrate multi-source heterogeneous data. In this paper, we present one application of the knowledge graph in the upstream of PetroChina. Specifically, we first construct a knowledge graph from both structured and unstructured data with multiple NLP (n(...) | Scopus | 2020 | 10.1007/s11390-020-9966-7 | Zhou X.-G., Gong R.-B., Shi F.-G., Wang Z.-F. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085188751&doi=10.1007%2fs11390-020-9966-7&partnerID=40&md5=b37619827a8a9eab96d99350c864143e | China | entity extraction, relation extraction, entity linking, semantic search | evaluation research | tool | energy |
| Journal Article | Predictive Article Recommendation Using Natural Language Processing and Machine Learning to Support Evidence Updates in Domain-Specific Knowledge Graphs | Artificial intelligence; Machine learning; Natural language processing; Precision medicine(...) | Objectives: Describe an augmented intelligence approach to facilitate the update of evidence for associations in knowledge graphs. Methods: New publications are filtered through multiple machine learning study classifiers, and filtered publications are combined with articles already included as evidence in the knowledge graph. The corpus is then subjected to named entity recognition, semantic dictionary mapping, term vector space modeling, pairwise similarity, and focal entity match to identify (...) | Scopus | 2020 | 10.1093/jamiaopen/ooaa028 | Sharma B., Willis V.C., Huettner C.S., Beaty K., Snowdon J.L., Xue S., South B.R., Jackson G.P., Weeraratne D., Michelini V. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102082317&doi=10.1093%2fJAMIAOPEN%2fOOAA028&partnerID=40&md5=96cd196b2a667e74a01cef611e6ad2c1 | United States | semantic search | validation research | method | health |
| Conference Paper | Pretrain-Kge: Learning Knowledge Representation from Pretrained Language Models | Computational linguistics; Knowledge graph; Knowledge management; Knowledge representation; Semantics; Graph embeddings; Knowledge graphs; Knowledge-representation; Language model; Performance degradation; Phase semantics; Three phase; Three phasis; Training framework; World knowledge; Graph embeddings(...) | Conventional knowledge graph embedding (KGE) often suffers from limited knowledge representation, leading to performance degradation especially on the low-resource problem. To remedy this, we propose to enrich knowledge representation via pretrained language models by leveraging world knowledge from pretrained models. Specifically, we present a universal training framework named Pretrain-KGE consisting of three phases: semantic-based fine-tuning phase, knowledge extracting phase and KGE training(...) | ACL | 2020 | - | Zhang Z., Liu X., Zhang Y., Su Q., Sun X., He B. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106358658&partnerID=40&md5=e5da2b0b8b86d1fae0a8ebde4a62aff9 | China | knowledge graph embedding | validation research | method | - |
| Conference Paper | Proactive Human-Machine Conversation with Explicit Conversation Goals | Speech processing; Baseline results; Baseline systems; Conversation systems; Conversational agents; Dialogue models; Dialogue systems; Knowledge graphs; State of the art; Computational linguistics(...) | Though great progress has been made for human-machine conversation, current dialogue system is still in its infancy: it usually converses passively and utters words more as a matter of response, rather than on its own initiatives. In this paper, we take a radical step towards building a human-like conversational agent: endowing it with the ability of proactively leading the conversation (introducing a new topic or maintaining the current topic). To facilitate the development of such conversation(...) | ACL | 2020 | - | Wu W., Guo Z., Zhou X., Wu H., Zhang X., Lian R., Wang H. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084072500&partnerID=40&md5=c8c271ac709cf166e1a5f7a3c2be9908 | China | conversational interfaces | validation research | resource | - |
| Conference Paper | Question Answering System Based on Knowledge Graph of Film Culture | Film Culture; Knowledge Graph; Natural Language Processing; Question Answering System(...) | The research and development of intelligent question answering system in today's society is more and more fierce, and it has more and more extensive application prospects. Different from the traditional question answering system which is more biased towards document retrieval, the question answering system based on knowledge graph can accurately identify the user's intention and give accurate answers. This article builds an intelligent Chinese question and answering system for the field of film (...) | IEEE | 2020 | 10.1109/iccst50977.2020.00035 | Shuai Q., Zhang C. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099686809&doi=10.1109%2fICCST50977.2020.00035&partnerID=40&md5=5826077c8e82b01afa68e33d8e96ff83 | China | entity extraction, relation extraction, question answering | solution proposal | tool | culture |
| Conference Paper | Question Answering System over Knowledge Graph of Weapon Field | knowledge graph; natural language processing; question answering; weapon field(...) | Question answering system in the weapon field not only enables users to obtain information on weapons quickly and accurately, but also provides smarter question answering. With military weapons as the research direction, an SVM question classification method based on Chinese character algorithm is proposed, and a question answering system over knowledge graph of weapons is established. The domain word segmentation is used in this system to analyze user questions, extract question features for cl(...) | IEEE | 2020 | 10.1109/crc51253.2020.9253485 | Gao P., Zhao T. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097828203&doi=10.1109%2fCRC51253.2020.9253485&partnerID=40&md5=ae75435b73af7d9b778a949210f71121 | China | question answering, text classification | validation research | method | public sector |
| Conference Paper | Recurrent Event Network: Autoregressive Structure Inferenceover Temporal Knowledge Graphs | - | Knowledge graph reasoning is a critical task in natural language processing. The task becomes more challenging on temporal knowledge graphs, where each fact is associated with a timestamp. Most existing methods focus on reasoning at past timestamps and they are not able to predict facts happening in the future. This paper proposes Recurrent Event Network (RE-Net), a novel autoregressive architecture for predicting future interactions. The occurrence of a fact (event) is modeled as a probability (...) | ACL | 2020 | 10.18653/v1/2020.emnlp-main.541 | Jin, Woojeong and Qu, Meng and Jin, Xisen and Ren, Xiang | https://aclanthology.org/2020.emnlp-main.541 | Canada | knowledge graph embedding, link prediction | validation research | technique | - |
| Journal Article | Relation Classification Via Knowledge Graph Enhanced Transformer Encoder | Knowledge graph embedding; Relation classification; Transformer(...) | Relation classification is an important task in natural language processing fields. The goal is to predict predefined relations for the marked nominal pairs in given sentences. State-of-the-art works usually focus on using deep neural networks as classifier to conduct the relation prediction. The rich semantic information of relationships in the triples of existing knowledge graph (KG) can be used as additional supervision for relation classification. However, these relationships were simply use(...) | ScienceDirect | 2020 | 10.1016/j.knosys.2020.106321 | Huang W., Mao Y., Yang Z., Zhu L., Long J. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089218225&doi=10.1016%2fj.knosys.2020.106321&partnerID=40&md5=86586076140461c8cbd7c95cd7997255 | China | relation classification, augmented language models | validation research | technique | - |
| Conference Paper | Relation Extraction Using Language Model Based on Knowledge Graph | Knowledge graph; Language model; Relation extraction(...) | Relation extraction is an important task in natural language processing (NLP). The existing methods generally pay more attention on extracting textual semantic information from text, but ignore the relation contextual information from existed relations in datasets, which is very important for the performance of relation extraction task. In this paper, we represent each individual entity as a embedding based on entities and relations knowledge graph, which encodes the relation contextual informat(...) | Scopus | 2020 | 10.1088/1742-6596/1624/2/022037 | Xing C., Liu X., Du D., Hu W., Zhang M. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096414931&doi=10.1088%2f1742-6596%2f1624%2f2%2f022037&partnerID=40&md5=8dabd909fb79c05a8f685bdafcfba007 | China | relation extraction, augmented language models | validation research | technique | - |
| Journal Article | Representation Learning of Knowledge Graphs with Embedding Subspaces | Embeddings; Encoding (symbols); Natural language processing systems; Semantics; Vectors; Feature vectors; Knowledge graphs; Natural language model; Prediction errors; Semantic properties; Supervised methods; Training data; Unstructured texts; Knowledge representation(...) | Most of the existing knowledge graph embedding models are supervised methods and largely relying on the quality and quantity of obtainable labelled training data. The cost of obtaining high quality triples is high and the data sources are facing a serious problem of data sparsity, which may result in insufficient training of long-tail entities. However, unstructured text encoding entities and relational knowledge can be obtained anywhere in large quantities. Word vectors of entity names estimate(...) | Scopus | 2020 | 10.1155/2020/4741963 | Li C., Xian X., Ai X., Cui Z. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092061749&doi=10.1155%2f2020%2f4741963&partnerID=40&md5=7a7df2a1da68d4a421df0133445fd790 | China | knowledge graph embedding, entity classification | validation research | technique | - |
| Conference Paper | Research and Implementation of Intelligent Question Answering System Based on Knowledge Graph of Traditional Chinese Medicine | intelligent question answering; knowledge graph; Knowledge of traditional Chinese medicine; Neo4j(...) | The combination of knowledge graph and natural language processing technology has become more and more widely used, and it has become one of the areas that major search engine companies attach importance to. Despite the steady progress of scientific and technological innovation and popularization of traditional Chinese medicine(TCM) knowledge, how to visualize and analyze complex TCM information in the field of TCM is still a difficult problem to solve. To this end, this research is based on the(...) | IEEE | 2020 | 10.23919/ccc50068.2020.9189518 | Zou Y., He Y., Liu Y. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091401695&doi=10.23919%2fCCC50068.2020.9189518&partnerID=40&md5=0e1dbf8849cbe7a398980d686bf4016e | China, Mongolia | question answering | solution proposal | tool | health |
| Conference Paper | Research and Implementation of Qa System Based on the Knowledge Graph of Chinese Classic Poetry | Chinese classical poetry; Knowledge graph; Natural language processing; QA system(...) | With the rapid development of the Internet, intelligent QA (Question Answering) system has been widely used in telecom operators, financial services, e-commerce shopping and other industries, but there are few researches and applications of intelligent QA system in the field of Chinese classical poetry. In view of the above situation, this paper aims to implement an automatic QA system based on the knowledge graph of Chinese classical poetry by combining natural language processing technology. I(...) | IEEE | 2020 | 10.1109/icccbda49378.2020.9095587 | Chen Z., Yin S., Zhu X. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085735617&doi=10.1109%2fICCCBDA49378.2020.9095587&partnerID=40&md5=d2f42283770bc3625fbd5d9e6916928d | China | question answering, conversational interfaces | solution proposal | tool | culture |
| Conference Paper | Research on Key Technologies of Knowledge Graph Construction Based on Natural Language Processing | Character recognition; Data mining; Extraction; Knowledge representation; Entity disambiguation; Entity recognition; Keyword extraction; NAtural language processing; Natural Language Processing Tools; Relationship extraction; Word discoveries; Word segmentation; Natural language processing systems(...) | As we all know, building a domain knowledge graph from a large amount of text requires a very large amount of work, including entity recognition, entity disambiguation, relationship extraction, and event extraction, etc. It is difficult to build a very comprehensive domain knowledge graph from scratch. Fortunately, with the rapid progress of natural language processing technology, we can use a large number of natural language processing tools to help us build a domain knowledge graph. This artic(...) | Scopus | 2020 | 10.1088/1742-6596/1601/3/032057 | Wang G., Tao Y., Ma H., Bao T., Yang J. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091287524&doi=10.1088%2f1742-6596%2f1601%2f3%2f032057&partnerID=40&md5=8136a7566a17df25bc8e470ca2a3eb12 | China | entity extraction | validation research | technique | - |
| Conference Paper | Research on Tourism Question Answering System Based on Xi'An Tourism Knowledge Graph | Big data; Convolutional neural networks; Knowledge representation; Multilayer neural networks; Natural language processing systems; Attention mechanisms; Design and implements; Input layers; Knowledge graphs; Natural language questions; Professional fields; Question answering systems; Similarity calculation; Tourism(...) | Question answering (QA) system provides a direct, efficient and accurate way for people to obtain information. At present, open domain QA systems such as Siri and Cortana are widely used in the general field, but they cannot meet the demand of some professional fields. This paper focuses on the background and needs of QA in the tourism field, researching the relevant technologies required for the implementation of QA system, and finally completes the construction of QA system based on the knowle(...) | Scopus | 2020 | 10.1088/1742-6596/1616/1/012090 | Yang L., Cao H., Hao F., Zhang W., Ahmad M. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090498054&doi=10.1088%2f1742-6596%2f1616%2f1%2f012090&partnerID=40&md5=474d6f60380c783424e083b07d32a932 | China | question answering | solution proposal | tool | tourism |
| Conference Paper | Salkg: a Semantic Annotation System for Building a High-Quality Legal Knowledge Graph | Annotation System; Knowledge Graph; Legal Text; Semantic Annotation(...) | Knowledge graph has become an essential tool for semantic analysis with the development of natural language processing and deep learning. A high-quality knowledge graph is handy for building a high-performance knowledge-driven application. Despite recent advances in information extraction (IE) techniques, no suitable automated methods can be applied to constructing a domain-specific, comprehensive, and high-quality knowledge graph. However, a semi-automatic strategy, which can ensure the basic q(...) | IEEE | 2020 | 10.1109/bigdata50022.2020.9378107 | Tang M., Su C., Chen H., Qu J., Ding J. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103843885&doi=10.1109%2fBigData50022.2020.9378107&partnerID=40&md5=82016294baea2b82bb3e0ce426518263 | China | entity extraction, relation extraction, ontology construction | validation research | tool | law |
| Conference Paper | Scalable Multi-Hop Relational Reasoning for Knowledge-Aware Question Answering | Computational linguistics; Graph neural networks; Graph theory; Natural language processing systems; Scalability; Based reasonings; External knowledge; Knowledge graphs; Language model; Model prediction; Multi-hops; Path-based; Question Answering; Relational reasoning; Subgraphs; Knowledge graph(...) | Existing work that augment question answering (QA) models with external knowledge (e.g., knowledge graphs) either struggle to model multi-hop relations efficiently, or lack transparency into the model's prediction rationale. In this paper, we propose a novel knowledge-aware approach that equips pretrained language models (PTLMs) with a multi-hop relational reasoning module, named multi-hop graph relation network (MHGRN). It performs multi-hop, multi-relational reasoning over subgraphs extracted (...) | ACL | 2020 | - | Feng Y., Chen X., Lin B.Y., Wang P., Yan J., Ren X. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106108628&partnerID=40&md5=1de6dd7317d0329648c9dd14d033d6a0 | China, United States | question answering | validation research | tool; resource | - |
| Conference Paper | Self-Supervised Knowledge Triplet Learning for Zero-Shot Question Answering | Computational linguistics; 'current; Commonsense knowledge; Data annotation; Knowledge graphs; Question Answering; Question answering systems; Scientific knowledge; Supervised methods; Synthetic graphs; System focus; Knowledge graph(...) | The aim of all Question Answering (QA) systems is to generalize to unseen questions. Current supervised methods are reliant on expensive data annotation. Moreover, such annotations can introduce unintended annotator bias, making systems focus more on the bias than the actual task. This work proposes Knowledge Triplet Learning (KTL), a self-supervised task over knowledge graphs. We propose heuristics to create synthetic graphs for commonsense and scientific knowledge. We propose using KTL to perf(...) | ACL | 2020 | - | Banerjee P., Baral C. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099810077&partnerID=40&md5=923557d7b4307909d7280409e37ea35d | United States | question answering | validation research | technique | - |
| Journal Article | Semantic Publication of Agricultural Scientific Literature Using Property Graphs | Digital publishing; Knowledge graph; Literature search; Property graph; Semantic web(...) | During the last decades, there have been significant changes in science that have provoked a big increase in the number of articles published every year. This increment implies a new difficulty for scientists, who have to do an extra effort for selecting literature relevant for their activity. In this work, we present a pipeline for the generation of scientific literature knowledge graphs in the agriculture domain. The pipeline combines SemanticWeb and natural language processing technologies, w(...) | Scopus | 2020 | 10.3390/app10030861 | Abad-Navarro F., Bernabé-Diaz J.A., García-Castro A., Fernandez-Breis J.T. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081533013&doi=10.3390%2fapp10030861&partnerID=40&md5=154297ed2705465af62db64c08e57165 | Germany, Spain | entity extraction, relation extraction, ontology construction, semantic search | solution proposal | method | agriculture |
| Journal Article | Semantic Similarity Estimation Using Vector Symbolic Architectures | Concept representation; semantic similarity; vector symbolic architectures; word embeddings(...) | For many natural language processing applications, estimating similarity and relatedness between words are key tasks that serve as the basis for classification and generalization. Currently, vector semantic models (VSM) have become a fundamental language modeling tool. VSMs represent words as points in a high-dimensional space and follow the distributional hypothesis of meaning, which assumes that semantic similarity is related to the context. In this paper, we propose a model whose representati(...) | IEEE | 2020 | 10.1109/access.2020.3001765 | Quiroz-Mercado J.I., Barron-Fernandez R., Ramirez-Salinas M.A. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087327864&doi=10.1109%2fACCESS.2020.3001765&partnerID=40&md5=8358776fa31a03fcb0c297e47ff127e0 | Mexico | augmented language models, semantic similarity | validation research | technique | - |
| Journal Article | Semi-Automatic Corpus Expansion and Extraction of Uyghur-Named Entities and Relations Based on a Hybrid Method | Conditional randomfield; Hybrid neural network; Named entity; Relation extraction; Uyghur(...) | Relation extraction is an important task with many applications in natural language processing, such as structured knowledge extraction, knowledge graph construction, and automatic question answering system construction. However, relatively little past work has focused on the construction of the corpus and extraction of Uyghur-named entity relations, resulting in a very limited availability of relation extraction research and a deficiency of annotated relation data. This issue is addressed in th(...) | Scopus | 2020 | 10.3390/info11010031 | Halike A., Abiderexiti K., Yibulayin T. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079035010&doi=10.3390%2finfo11010031&partnerID=40&md5=da2537c978af69c328a8600a11cff1c2 | China | entity extraction, relation extraction | validation research | technique | - |
| Conference Paper | Seq2Kg: an End-To-End Neural Model for Domain Agnostic Knowledge Graph (Not Text Graph) Construction from Text | Classification (of information); Deep learning; Deep neural networks; Knowledge based systems; Natural language processing systems; Semantics; Annotated datasets; Downstream applications; Evaluation metrics; Learning neural networks; Multi label classification; NAtural language processing; Statistical pattern; Unstructured texts; Knowledge representation(...) | Knowledge Graph Construction (KGC) from text unlocks information held within unstructured text and is critical to a wide range of downstream applications. General approaches to KGC from text are heavily reliant on the existence of knowledge bases, yet most domains do not even have an external knowledge base readily available. In many situations this results in information loss as a wealth of key information is held within "non-entities". Domain-specific approaches to KGC typically adopt unsuperv(...) | Scopus | 2020 | - | Stewart M., Liu W. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104651859&partnerID=40&md5=b6bc02d172c25b1db434ecd16ac178c4 | Australia | entity extraction, relation extraction, entity linking | validation research | tool; resource | - |
| Conference Paper | Skos Tool: a Tool for Creating Knowledge Graphs to Support Semantic Text Classification | Artificial intelligence; Knowledge graph; Natural language processing; Semantic classifier; SKOS(...) | Knowledge graphs are being increasingly adopted in industry in order to add meaning to data and improve the intelligence of data analytics methods. Simple Knowledge Management System (SKOS) is a W3C standard for representation of knowledge graphs in a web-native and machine-understandable format. This paper introduces SKOS Tool; a web-based application developed at the Engineering Informatics Lab at Texas State University. It can be used for creating knowledge graphs and concept schemes based on(...) | Scopus | 2020 | 10.1007/978-3-030-57997-5_31 | Ameri F., Yoder R., Zandbiglari K. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090171505&doi=10.1007%2f978-3-030-57997-5_31&partnerID=40&md5=aa9f9b0ad98ed95b771194f7f5f30b35 | United States | text classification, ontology construction | solution proposal | tool | - |
| Conference Paper | Tackling Long-Tailed Relations and Uncommon Entities in Knowledge Graph Completion | Knowledge representation; Generative model; Knowledge graphs; Learning paradigms; Meta-learning frameworks; Real-world; Recent researches; Textual description; Natural language processing systems(...) | For large-scale knowledge graphs (KGs), recent research has been focusing on the large proportion of infrequent relations which have been ignored by previous studies. For example few-shot learning paradigm for relations has been investigated. In this work, we further advocate that handling uncommon entities is inevitable when dealing with infrequent relations. Therefore, we propose a meta-learning framework that aims at handling infrequent relations with few-shot learning and uncommon entities b(...) | ACL | 2020 | - | Wang Z., Lai K.P., Li P., Bing L., Lam W. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084307036&partnerID=40&md5=15932c5f9d05c9e5672a64d002a86c98 | China, Hong Kong, Singapore | entity extraction, relation extraction, attribute extraction | validation research | tool | - |
| Journal Article | Technet: Technology Semantic Network Based on Patent Data | Knowledge discovery, Word embedding, Technology semantic network, Knowledge representation(...) | The growing developments in general semantic networks, knowledge graphs and ontology databases have motivated us to build a large-scale comprehensive semantic network of technology-related data for engineering knowledge discovery, technology search and retrieval, and artificial intelligence for engineering design and innovation. Specially, we constructed a technology semantic network (TechNet) that covers the elemental concepts in all domains of technology and their semantic associations by mini(...) | ScienceDirect | 2020 | 10.1016/j.eswa.2019.112995 | Serhad Sarica and Jianxi Luo and Kristin L. Wood | https://www.sciencedirect.com/science/article/pii/S0957417419307122 | Singapore | entity extraction, relation extraction | solution proposal | tool | engineering |
| Conference Paper | Towards Context-Aware Knowledge Entailment from Health Conversations | Knowledge representation; Natural language processing systems; Back-ground knowledge; Contextualized knowledge; Conversational agents; Domain-specific ontologies; Machine learning approaches; NAtural language processing; Reasoning capabilities; Recognizing textual entailments; Learning systems(...) | Despite the competitive efforts of leading companies, cognitive technologies such as chatbot technologies still have limited cognitive capabilities. One of the major challenges that they face is knowledge entailment from the ongoing conversations with a user. Knowledge entailment implies entailing facts that indicate opinions, beliefs, expressions, requests, and feelings of a particular user about a particular target during conversations. The entailed pieces of knowledge will evolve the backgrou(...) | Scopus | 2020 | - | Shekarpour S., Alshargi F., Shekarpour M. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108841003&partnerID=40&md5=35f7cdc597ce11e21dc1f7e1873df653 | Germany, United States | conversational interfaces, natural language inference | solution proposal | method | health |
| Journal Article | Towards Knowledge Enhanced Language Model for Machine Reading Comprehension | BERT; capsule network; knowledge graph embedding; Machine reading comprehension(...) | Machine reading comprehension is a crucial and challenging task in natural language processing (NLP). Recently, knowledge graph (KG) embedding has gained massive attention as it can effectively provide side information for downstream tasks. However, most previous knowledge-based models do not take into account the structural characteristics of the triples in KGs, and only convert them into vector representations for direct accumulation, leading to deficiencies in knowledge extraction and knowled(...) | IEEE | 2020 | 10.1109/access.2020.3044308 | Gong P., Liu J., Yang Y., He H. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098272414&doi=10.1109%2fACCESS.2020.3044308&partnerID=40&md5=d21eb52d52816118ac8b83407888c247 | China | question answering, augmented language models | validation research | technique | - |
| Conference Paper | Towards Medical Machine Reading Comprehension with Structural Knowledge and Plain Text | Computational linguistics; Diagnosis; Large dataset; Comprehension models; Language model; Large-scales; Medical fields; Medical knowledge; Multi choices; Plain text; Reading comprehension; Structural knowledge; Training data; Knowledge graph(...) | Machine reading comprehension (MRC) has achieved significant progress on the open domain in recent years, mainly due to large-scale pre-trained language models. However, it performs much worse in specific domains such as the medical field due to the lack of extensive training data and professional structural knowledge neglect. As an effort, we first collect a large scale medical multi-choice question dataset (more than 21k instances) for the National Licensed Pharmacist Examination in China. It (...) | ACL | 2020 | - | Li D., Hu B., Chen Q., Peng W., Wang A. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100236546&partnerID=40&md5=15e2103d03284c26bdf2a9017c3d48a6 | China | question answering, augmented language models | validation research | technique | health |
| Conference Paper | Uncovering Semantic Bias in Neural Network Models Using a Knowledge Graph | explainable AI; knowledge graphs; neural networks; rule mining(...) | While neural networks models have shown impressive performance in many NLP tasks, lack of interpretability is often seen as a disadvantage. Individual relevance scores assigned by post-hoc explanation methods are not sufficient to show deeper systematic preferences and potential biases of the model that apply consistently across examples. In this paper we apply rule mining using knowledge graphs in combination with neural network explanation methods to uncover such systematic preferences of trai(...) | Scopus | 2020 | 10.1145/3340531.3412009 | Nikolov A., D'Aquin M. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095864404&doi=10.1145%2f3340531.3412009&partnerID=40&md5=83d27193d26e776ef22a6c22710015e8 | Ireland | text classification | validation research | method | - |
| Conference Paper | Unscripted Conversation through Knowledge Graph | Conversational AI; Knowledge Graph; Natural Language Processing(...) | In this paper, we introduce "unscripted conversation" - free form dialog over a domain knowledge graph. We describe a use case around Luggage handling for a commercial airline where we answer users queries regarding various policies such as luggage dimensions, restrictions on carry-on items, travel routes etc. We have encoded the domain entities, relationships, processes and polices in the knowledge graph and created a generic semantic natural language processing engine to process user queries a(...) | Scopus | 2020 | - | Ramnani R.R., Sengupta S., Gakhar A., Maheshwari S., Mitra S. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096232985&partnerID=40&md5=2798bb051049b396493c4f360e1d17f8 | - | conversational interfaces, question answering | solution proposal | method | business |
| Journal Article | Using Character-Level and Entity-Level Representations to Enhance Bidirectional Encoder Representation from Transformers-Based Clinical Semantic Textual Similarity Model: Clinicalsts Modeling Study | Clinical semantic textual similarity; Deep learning; Knowledge graph; Natural language processing(...) | Background: With the popularity of electronic health records (EHRs), the quality of health care has been improved. However, there are also some problems caused by EHRs, such as the growing use of copy-and-paste and templates, resulting in EHRs of low quality in content. In order to minimize data redundancy in different documents, Harvard Medical School and Mayo Clinic organized a national natural language processing (NLP) clinical challenge (n2c2) on clinical semantic textual similarity (Clinica(...) | Scopus | 2020 | 10.2196/23357 | Xiong Y., Chen S., Chen Q., Yan J., Tang B. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098537330&doi=10.2196%2f23357&partnerID=40&md5=3bffae3ee2d61d23c487f717aaf44ee1 | China | semantic similarity, augmented language models | validation research | technique | health |
| Conference Paper | Using Local Knowledge Graph Construction to Scale Seq2Seq Models to Multi-Document Inputs | Graph structures; Knowledge based systems; Knowledge representation; Query processing; Graph representation; Information synthesis; Local knowledge; Multi-document; Multi-document summarization; Question Answering; Sequence models; Structured knowledge; Natural language processing systems(...) | Query-based open-domain NLP tasks require information synthesis from long and diverse web results. Current approaches extractively select portions of web text as input to Sequence-to-Sequence models using methods such as TF-IDF ranking. We propose constructing a local graph structured knowledge base for each query, which compresses the web search information and reduces redundancy. We show that by linearizing the graph into a structured input sequence, models can encode the graph representations(...) | ACL | 2020 | - | Fan A., Gardent C., Braud C., Bordes A. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082548175&partnerID=40&md5=b73972fec8bc7474af4e72b788c4d693 | France | text summarization, augmented language models | validation research | technique | - |
| Conference Paper | Visual Analysis and Mining of Knowledge Graph for Power Network Data Based on Natural Language Processing | data mining; knowledge graph; Natural language processing; power network; visual analysis(...) | Visual analysis and mining of knowledge graph for power network data based on the natural language processing is proposed in this study. Intelligent substation, through the main equipment intelligence, the primary system modularization, the secondary system integration, the communication system network, realizes the remote centralized control to the substation operation adjustment and the electrical operation 'one-click' automatic completion. Hence, this paper has 2 core novelties. (1) Under the(...) | IEEE | 2020 | 10.1109/iccmc48092.2020.iccmc-00077 | Zhao L., Zhao Z., Xu H., Zhang Y., Xu Y. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084660309&doi=10.1109%2fICCMC48092.2020.ICCMC-00077&partnerID=40&md5=aac16f7e9eb46623567ae79e224b52a8 | China | entity extraction, relation extraction, semantic search | solution proposal | method | energy |
| Journal Article | Winfra: a Web-Based Platform for Semantic Data Retrieval and Data Analytics | Association rules; Data mining; Heterogeneous data federation; Knowledge graph; Natural language processing; RDF(...) | Given the huge amount of heterogeneous data stored in different locations, it needs to be federated and semantically interconnected for further use. This paper introduces WINFRA, a comprehensive open-access platform for semantic web data and advanced analytics based on natural language processing (NLP) and data mining techniques (e.g., association rules, clustering, classification based on associations). The system is designed to facilitate federated data analysis, knowledge discovery, informati(...) | Scopus | 2020 | 10.3390/math8112090 | Ait-Mlouk A., Vu X.-S., Jiang L. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096502286&doi=10.3390%2fmath8112090&partnerID=40&md5=7d7621948f38b3dbe5c2d4dccf37e982 | Sweden | entity extraction, entity linking, semantic search | solution proposal | tool | - |
| Conference Paper | Zero-Shot Word Sense Disambiguation Using Sense Definition Embeddings | Computational linguistics; Embeddings; Signal encoding; Knowledge graphs; Label space; Large corpora; NAtural language processing; Poor performance; State of the art; Word-sense disambiguation; Wordnet; Natural language processing systems(...) | Word Sense Disambiguation (WSD) is a longstanding but open problem in Natural Language Processing (NLP). WSD corpora are typically small in size, owing to an expensive annotation process. Current supervised WSD methods treat senses as discrete labels and also resort to predicting the Most-Frequent-Sense (MFS) for words unseen during training. This leads to poor performance on rare and unseen senses. To overcome this challenge, we propose Extended WSD Incorporating Sense Embeddings (EWISE), a sup(...) | ACL | 2020 | - | Kumar S., Jat S., Saxena K., Talukdar P. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075859601&partnerID=40&md5=df7fa442077c5b74e0de03860cb4219b | India, United States | text analysis, augmented language models | validation research | technique | - |
| Conference Paper | A Chinese Machine Reading Comprehension Dataset Automatic Generated Based on Knowledge Graph | Knowledge graph; Machine reading comprehension; PLMs(...) | Machine reading comprehension (MRC) is a typical natural language processing (NLP) task and has developed rapidly in the last few years. Various reading comprehension datasets have been built to support MRC studies. However, large-scale and high-quality datasets are rare due to the high complexity and huge workforce cost of making such a dataset. Besides, most reading comprehension datasets are in English, and Chinese datasets are insufficient. In this paper, we propose an automatic method for M(...) | ACL | 2021 | 10.1007/978-3-030-84186-7_18 | Zhao H., Yuan S., Leng J., Pan X., Xue Z., Ma Q., Liang Y. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113578050&doi=10.1007%2f978-3-030-84186-7_18&partnerID=40&md5=e17697829fea1c40d22e12c9ef982cff | China, United States | question answering, question generation | validation research | method; resource | health |
| Journal Article | A Framework to Extract Biomedical Knowledge from Gluten-Related Tweets: the Case of Dietary Concerns in Digital Era | Graph mining; Health for informatics; Machine learning; Social media; Sociome profiling; Text mining(...) | Big data importance and potential are becoming more and more relevant nowadays, enhanced by the explosive growth of information volume that is being generated on the Internet in the last years. In this sense, many experts agree that social media networks are one of the internet areas with higher growth in recent years and one of the fields that are expected to have a more significant increment in the coming years. Similarly, social media sites are quickly becoming one of the most popular platfor(...) | ScienceDirect | 2021 | 10.1016/j.artmed.2021.102131 | Pérez-Pérez M., Igrejas G., Fdez-Riverola F., Lourenço A. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114659514&doi=10.1016%2fj.artmed.2021.102131&partnerID=40&md5=f2518ad0b1f7b62c04cea3d38dff886f | Spain, Portugal | semantic search | solution proposal | method | social media; health |
| Journal Article | A Heuristic Grafting Strategy for Manufacturing Knowledge Graph Extending and Completion Based on Nature Language Processing: Knowtree | heuristic grafting strategy (HGS); Knowledge graph extending and completion; NLP(...) | Applied to search, question answering, and semantic web of close-or-open domain, knowledge graph (KG) is known for its incompleteness subject to the rapid knowledge growing pace. Inspired by the agricultural grafting technology to fruit variety, this paper proposes a heuristic knowledge grafting strategy (HGS) for manufacturing knowledge graph (MKG) named KnowTree extending and completion with natural language processing (NLP) mining engineering cases document. Based on similarity analysis, firs(...) | IEEE | 2021 | 10.1109/access.2021.3092019 | He L., Dong B., Jiang P. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112701844&doi=10.1109%2fACCESS.2021.3092019&partnerID=40&md5=8962f4344fb4181f1aedae10d9802f83 | China, United States | entity classification, link prediction | validation research | method | engineering |
| Journal Article | A Joint Model for Representation Learning of Tibetan Knowledge Graph Based on Encyclopedia | encyclopedia; joint model; knowledge graph; representation learning; Tibetan(...) | Learning the representation of a knowledge graph is critical to the field of natural language processing. There is a lot of research for English knowledge graph representation. However, for the low-resource languages, such as Tibetan, how to represent sparse knowledge graphs is a key problem. In this article, aiming at scarcity of Tibetan knowledge graphs, we extend the Tibetan knowledge graph by using the triples of the high-resource language knowledge graphs and Point of Information map inform(...) | ACM | 2021 | 10.1145/3447248 | Sun Y., Chen A., Chen C., Xia T., Zhao X. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105736079&doi=10.1145%2f3447248&partnerID=40&md5=ec1145a61467f2bf4e7a4f8ccd6e87ad | China | knowledge graph embedding | validation research | technique | - |
| Journal Article | A Knowledge Graph Based Question Answering Method for Medical Domain | Artificial Intelligence; Data Mining and Machine Learning; Knowledge graph; Medical domain; Natural Language and Speech; Question answering; Weighted path ranking(...) | Question answering (QA) is a hot field of research in Natural Language Processing. A big challenge in this field is to answer questions from knowledge-dependable domain. Since traditional QA hardly satisfies some knowledge-dependable situations, such as disease diagnosis, drug recommendation, etc. In recent years, researches focus on knowledge-based question answering (KBQA). However, there still exist some problems in KBQA, traditional KBQA is limited by a range of historical cases and takes to(...) | Scopus | 2021 | 10.7717/peerj-cs.667 | Huang X., Zhang J., Xu Z., Ou L., Tong J. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116399084&doi=10.7717%2fpeerj-cs.667&partnerID=40&md5=5ae8448c08857ac21d10e4493b8d3b8b | China | question answering | validation research | method | health |
| Journal Article | A Knowledge Graph Embedding Approach for Metaphor Processing | Knowledge graph embedding; Metaphor detection; Metaphor generation; Metaphor interpretation; Metaphor processing(...) | Metaphor is a figure of speech that describes one thing (a target) by mentioning another thing (a source) in a way that is not literally true. Metaphor understanding is an interesting but challenging problem in natural language processing. This paper presents a novel method for metaphor processing based on knowledge graph (KG) embedding. Conceptually, we abstract the structure of a metaphor as an attribute-dependent relation between the target and the source. Each specific metaphor can be repres(...) | ACM | 2021 | 10.1109/taslp.2020.3040507 | Song W., Guo J., Fu R., Liu T., Liu L. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097935158&doi=10.1109%2fTASLP.2020.3040507&partnerID=40&md5=67852f0b08e3cbfc28ce33a0b4e4e50b | China | knowledge graph embedding, entity classification | validation research | technique | - |
| Conference Paper | A Knowledge Graph Question-Answering Platform Trained Independently of the Graph | Natural language processing systems; Dbpedia; Existing systems; Knowledge graphs; Natural language model; Question Answering; Three phase; Three phasis; Knowledge graph(...) | We will demonstrate KGQAn, a question-Answering platform trained independently of KGs. KGQAn transforms a question into semantically equivalent SPARQL queries via a novel three-phase strategy based on natural language models trained generally for understanding and leveraging short English text. Without preprocessing or annotated questions on KGs, KGQAn outperformed the existing systems in KG question answering by an improvement of at least 33% in F1-measure and 61% in precision. During the demo,(...) | Scopus | 2021 | - | Omar R., Dhall I., Sheikh N., Mansour E. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117682227&partnerID=40&md5=f983e996d9e20138bcab8939a67e8b75 | Canada | question answering | validation research | method | - |
| Journal Article | A Literature-Derived Knowledge Graph Augments the Interpretation of Single Cell Rna-Seq Datasets | Natural language processing; Single cell genomics(...) | Technology to generate single cell RNA-sequencing (scRNA-seq) datasets and tools to annotate them have advanced rapidly in the past several years. Such tools generally rely on existing transcriptomic datasets or curated databases of cell type defining genes, while the application of scalable natural language processing (NLP) methods to enhance analysis workflows has not been adequately explored. Here we deployed an NLP framework to objectively quantify associations between a comprehensive set of(...) | Scopus | 2021 | 10.3390/genes12060898 | Doddahonnaiah D., Lenehan P.J., Hughes T.K., Zemmour D., Garcia-Rivera E., Venkatakrishnan A.J., Chilaka R., Khare A., Kasaraneni A., Garg A., Anand A., Barve R., Thiagarajan V., Soundararajan V. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108405001&doi=10.3390%2fgenes12060898&partnerID=40&md5=ae16a148c6cecba93f21a2b11bf2c102 | India, United States | semantic search | validation research | technique | health |
| Journal Article | A Novel Word Similarity Measure Method for Iot-Enabled Healthcare Applications | Entropy; Healthcare; Internet of Things; Knowledge graph; Word embedding; Word similarity(...) | With the development of the Internet of Things (IoT), Natural Language Processing(NLP) has become a key part of IoT applications in Healthcare. NLP is bringing a revolutionary shift to Healthcare, powered by rapid progress of NLP analytics techniques and increasing availability of Healthcare data. Therefore, using NLP solution for IoT enable Healthcare application is an urgent and valuable task. Word similarity measurement is the basis of semantic analysis, which can be applied to translation an(...) | ScienceDirect | 2021 | 10.1016/j.future.2020.07.053 | Zhang D., Xia X., Yang Y., Yang P., Xie C., Cui M., Liu Q. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089150154&doi=10.1016%2fj.future.2020.07.053&partnerID=40&md5=6e6e20d1bab7c15958801aa8a4b7329d | China, United Kingdom | semantic similarity | validation research | technique | health; information technology |
| Conference Paper | A System for Automated Open-Source Threat Intelligence Gathering and Management | security knowledge graph; threat intelligence(...) | To remain aware of the fast-evolving cyber threat landscape, open-source Cyber Threat Intelligence (OSCTI) has received growing attention from the community. Commonly, knowledge about threats is presented in a vast number of OSCTI reports. Despite the pressing need for high-quality OSCTI, existing OSCTI gathering and management platforms, however, have primarily focused on isolated, low-level Indicators of Compromise. On the other hand, higher-level concepts (e.g., adversary tactics, techniques,(...) | Scopus | 2021 | 10.1145/3448016.3452745 | Gao P., Liu X., Choi E., Soman B., Mishra C., Farris K., Song D. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103051870&doi=10.1145%2f3448016.3452745&partnerID=40&md5=484b8c41fda8f0b37d0685d96c20feaf | United States | entity extraction, relation extraction, entity linking, semantic search | solution proposal | tool | information technology |
| Conference Paper | Ai-Supported Innovation Monitoring | Human-machine interaction; Hybrid AI; Innovation; Knowledge graph; Natural Language Processing; Policy-making(...) | Small and medium enterprises (SMEs) are a driving force for innovation. Stimulation of innovation in these SMEs is often the target of policy interventions, both regionally and nationally. Which technical areas should be in the focus and how to identify and monitor them? In this position paper, we propose hybrid AI methods for innovation monitoring, using natural language processing (NLP) and a dynamic knowledge graph that combines learning, reasoning and knowledge sharing in collaboration with (...) | Scopus | 2021 | 10.1007/978-3-030-73959-1_20 | Braaksma B., Daas P., Raaijmakers S., Geurts A., Meyer-Vitali A. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105908818&doi=10.1007%2f978-3-030-73959-1_20&partnerID=40&md5=808f6a86b179c6c1bbdae9c424e72581 | Netherlands | semantic search | opinion paper | method | business |
| Conference Paper | An Efficient Ros Package Searching Approach Powered by Knowledge Graph | Knowledge graph; NLP; ROS package searching(...) | Over the past several years, the Robot Operating System (ROS), has grown from a small research project into the most popular framework for robotics development. It offers a core set of software for operating robots that can be extended by creating or using existing packages, making it possible to program robotic software that can be reused on different hardware platforms. With thousands of packages available per stable distribution, encapsulating algorithms, sensor drivers, etc., it is the de fa(...) | Scopus | 2021 | 10.18293/seke2021-063 | Chen L., Mao X., Zhang Y., Yang S., Wang S. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114283258&doi=10.18293%2fSEKE2021-063&partnerID=40&md5=33e213145f09134302ef21743f6e61f0 | China | entity extraction, relation extraction, entity linking, semantic search | validation research | method | engineering |
| Journal Article | An Entity Linking Model Based on Candidate Features | Entity disambiguation; Entity linking; Knowledge graph(...) | Entity linking is a key step for automatic question and answering with knowledge graph. It has broad application prospects in Natural Language Processing, Information Retrieval and other fields. This paper constructed an entity linking model based on candidate features. Firstly, it proposed a candidate entities generation algorithm that combines knowledge base matching and word vector similarity calculation and then put forward a suitable entity disambiguation algorithm for different candidate e(...) | Scopus | 2021 | 10.1007/s13278-021-00761-z | Li D., Fu Z., Zheng Z. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107375872&doi=10.1007%2fs13278-021-00761-z&partnerID=40&md5=c332fb90e33c1955e882663e36b36a46 | China | entity linking | validation research | technique | - |
| Conference Paper | An Intelligent Question Answering System Based on Power Knowledge Graph | Natural language processing;knowledge graph;ontology schema;intelligent reasoning;intelligent question answering system(...) | The intelligent question answering (IQA) system can accurately capture users' search intention by understanding the natural language questions, searching relevant content efficiently from a massive knowledge-base, and returning the answer directly to the user. Since the IQA system can save inestimable time and workforce in data search and reasoning, it has received more and more attention in data science and artificial intelligence. This article introduced a domain knowledge graph using the grap(...) | IEEE | 2021 | 10.1109/pesgm46819.2021.9638018 | Y. Tang; H. Han; X. Yu; J. Zhao; G. Liu; L. Wei | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9638018 | China | question answering | solution proposal | tool | energy |
| Conference Paper | An Italian Question Answering System Based on Grammars Automatically Generated from Ontology Lexica | Knowledge graph; Natural language processing systems; Automatically generated; Dbpedia; Knowledge graphs; Model based approach; Ontology's; Question Answering; Question answering systems; Ontology(...) | The paper presents an Italian question answering system over linked data. We use a model-based approach to question answering based on an ontology lexicon in lemon format. The system exploits an automatically generated lexicalized grammar that can then be used to interpret and transform questions into SPARQL queries. We apply the approach for the Italian language and implement a question answering system that can answer more than 1.6 million questions over the DBpedia knowledge graph. © 2021 for(...) | Scopus | 2021 | - | Nolano G., Elahi M.F., di Buono M.P., Ell B., Cimiano P. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121222253&partnerID=40&md5=f725c57161f77956b7b474537756fec9 | Germany, Italy, Norway | question answering | solution proposal | tool | - |
| Conference Paper | An Overview of Relevant Literature on Different Approaches to Word Sense Disambiguation | Word Sense Disambiguation;Natural Language Processing;Lesk Algorithms;Embedding Techniques;Neural Network;Bi-LSTM;Knowledge Graph(...) | WSD (Word Sense Disambiguation) is a common issue in Natural Language Processing (NLP) and Machine Learning technology. In NLP, word sense disambiguation is described as the capacity to detect which meaning of a word is activated by its use in a specific context. WSD is a solution to the uncertainty that occurs when words have different meanings in different contexts. Contextual word meaning plays an important role in various applications such as sentiment analysis, search engine, information ex(...) | IEEE | 2021 | 10.1109/icecct52121.2021.9616677 | P. C. P; S. Mandal | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9616677 | India | text analysis | secondary research | guidelines | - |
| Conference Paper | Applying Curriculum Learning on Path-Based Knowledge Graph Reasoning Algorithems | Curriculum Learning; Knowledge Graph Reasoning; Natural Language Processing; Path-based Inferencing(...) | In the field of knowledge graph reasoning, path reasoning based on reinforcement learning avoids using random walking methods and the inefficient search, but what follows is the false path problem. The amount of false paths is more than that of correct ones. The agent would usually reach the correct entity from the wrong paths first, and be more inclined to them in subsequent exploration. We propose to use curriculum learning to solve this problem: assuming that in the environment corresponding (...) | IEEE | 2021 | 10.1109/icnlp52887.2021.00019 | Jia H., Luo L. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116154828&doi=10.1109%2fICNLP52887.2021.00019&partnerID=40&md5=ade73f64787a204652e47a171d0b44b3 | China | semantic search | validation research | technique | - |
| Conference Paper | Autokg - an Automotive Domain Knowledge Graph for Software Testing: a Position Paper | Automotive Domain Knowledge Graph; Natural Language Processing; Software Testing(...) | Industries have a significant amount of data in semi-structured and unstructured formats which are typically captured in text documents, spreadsheets, images, etc. This is especially the case with the software description documents used by domain experts in the automotive domain to perform tasks at various phases of the Software Development Life Cycle (SDLC). In this paper, we propose an end-to-end pipeline to extract an Automotive Knowledge Graph (AutoKG) from textual data using Natural Languag(...) | IEEE | 2021 | 10.1109/icstw52544.2021.00047 | Kesri V., Nayak A., Ponnalagu K. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108021658&doi=10.1109%2fICSTW52544.2021.00047&partnerID=40&md5=1ca3f1540a1e7c0912803defe84e593d | India | semantic search | opinion paper | method | engineering |
| Conference Paper | Automated Medical Reporting: from Multimodal Inputs to Medical Reports through Knowledge Graphs | Automated Reporting; Dialogue Interpretation; Electronic Medical Record; Healthcare Workflow Management; Knowledge Graphs; Patient Medical Graph(...) | Care providers generally experience a high workload mainly due to the large amount of time required for adequate documentation. This paper presents our visionary idea of real-time automated medical reporting through the integration of speech and action recognition technology with knowledge-based summarization of the interaction between care provider and patient. We introduce the Patient Medical Graph as a formal representation of the dialogue and actions during a medical consultation. This knowl(...) | Scopus | 2021 | - | Maas L., Kisjes A., Hashemi I., Heijmans F., Dalpiaz F., van Dulmen S., Brinkkemper S. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103819304&partnerID=40&md5=3fa4564632dd16ff158745ab59b96749 | Netherlands | conversational interfaces | solution proposal | tool | health |
| Journal Article | Automatic Detection of Covid-19 Vaccine Misinformation with Graph Link Prediction | COVID-19; knowledge graph embedding; Machine learning; Natural Language Processing; Social Media; vaccine misinformation(...) | Enormous hope in the efficacy of vaccines became recently a successful reality in the fight against the COVID-19 pandemic. However, vaccine hesitancy, fueled by exposure to social media misinformation about COVID-19 vaccines became a major hurdle. Therefore, it is essential to automatically detect where misinformation about COVID-19 vaccines on social media is spread and what kind of misinformation is discussed, such that inoculation interventions can be delivered at the right time and in the ri(...) | ScienceDirect | 2021 | 10.1016/j.jbi.2021.103955 | Weinzierl M.A., Harabagiu S.M. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119576283&doi=10.1016%2fj.jbi.2021.103955&partnerID=40&md5=a577d49a20361fe63db6c4e1c0c1b654 | United States | link prediction, knowledge graph embedding | validation research | technique; resource | social media; health |
| Conference Paper | Benchmarking Commonsense Knowledge Base Population with an Effective Evaluation Dataset | - | Reasoning over commonsense knowledge bases (CSKB) whose elements are in the form of free-text is an important yet hard task in NLP. While CSKB completion only fills the missing links within the domain of the CSKB, CSKB population is alternatively proposed with the goal of reasoning unseen assertions from external resources. In this task, CSKBs are grounded to a large-scale eventuality (activity, state, and event) graph to discriminate whether novel triples from the eventuality graph are plausibl(...) | ACL | 2021 | 10.18653/v1/2021.emnlp-main.705 | Fang, Tianqing and Wang, Weiqi and Choi, Sehyun and Hao, Shibo and Zhang, Hongming and Song, Yangqiu and He, Bin | https://aclanthology.org/2021.emnlp-main.705 | China, Hong Kong | triple classification, entity alignment | validation research | technique; resource | - |
| Journal Article | Bert Based Clinical Knowledge Extraction for Biomedical Knowledge Graph Construction and Analysis | Knowledge graph, Biomedical informatics, Clinical data, Natural language processing, BERT(...) | Background: Knowledge is evolving over time, often as a result of new discoveries or changes in the adopted methods of reasoning. Also, new facts or evidence may become available, leading to new understandings of complex phenomena. This is particularly true in the biomedical field, where scientists and physicians are constantly striving to find new methods of diagnosis, treatment and eventually cure. Knowledge Graphs (KGs) offer a real way of organizing and retrieving the massive and growing amo(...) | ScienceDirect | 2021 | 10.1016/j.cmpbup.2021.100042 | Ayoub Harnoune and Maryem Rhanoui and Mounia Mikram and Siham Yousfi and Zineb Elkaimbillah and Bouchra {El Asri} | https://www.sciencedirect.com/science/article/pii/S2666990021000410 | Morocco | entity extraction, relation extraction, question answering | solution proposal | method | health |
| Conference Paper | Bert-Based Semantic Query Graph Extraction for Knowledge Graph Question Answering | Complex networks; Natural language processing systems; Pipelines; Query processing; Recurrent neural networks; Semantics; Complex questions; Entity detection; Graph construction; Graph extractions; Knowledge graphs; Multi tasks; Query graph; Question Answering; Question Answering Task; Semantic query; Knowledge graph(...) | Answering complex questions involving multiple entities and relations remains a challenging Knowledge Graph Question Answering (KGQA) task. To extract a Semantic Query Graph (SQG), we propose a BERT-based decoder that is capable of jointly performing multi-Tasks for SQG construction, such as entity detection, relation prediction, output variable selection, query type classification and ordinal constraint detection. The outputs of our model can be seamlessly integrated with downstream components (...) | Scopus | 2021 | - | Liang Z., Peng Z., Yang X., Zhao F., Liu Y., McGuinness D.L. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117715122&partnerID=40&md5=401d5c9034f3970acc506efaa3a1d10e | China, United States | question answering, relation classification | validation research | technique | - |
| Conference Paper | Bert-Kg: a Short Text Classification Model Based on Knowledge Graph and Deep Semantics | Artificialintelligence; BERT-based model; Computermethodologies; Knowledge graph; Lexicalsemantics; Natural languageprocessing; Short textclassification(...) | Chinese short textclassification is one of the increasingly significant tasks inNatural Language Processing (NLP). Different from documents andparagraphs, short text faces the problems of shortness, sparseness,non-standardization, etc., which brings enormous challenges fortraditional classification methods. In this paper, we propose anovel model named BERT-KG, which can classify Chinese short textpromptly and accurately andovercome the difficulty of short text classification. BERT-KGenriches sho(...) | Scopus | 2021 | 10.1007/978-3-030-88480-2_58 | Zhong Y., Zhang Z., Zhang W., Zhu J. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118190056&doi=10.1007%2f978-3-030-88480-2_58&partnerID=40&md5=64c160df8300fa0dc98e0fa25883787c | China | text classification, augmented language models | validation research | technique | - |
| Conference Paper | Bertkg-Ddi: Towards Incorporating Entity-Specific Knowledge Graph Information in Predicting Drug-Drug Interactions | Embeddings; Knowledge representation; Natural language processing systems; Biomedical domain; Domain knowledge; Drug-drug interactions; Knowledge graphs; Natural language understanding; Relation classifications; Specific knowledge; State of the art; Drug interactions(...) | Off-the-shelf biomedical embeddings obtained from the recently released various pre-trained language models (such as BERT, XLNET) have demonstrated state-of-the-art results (in terms of accuracy) for the various natural language understanding tasks (NLU) in the biomedical domain. Relation Classification (RC) falls into one of the most critical tasks. In this paper, we explore how to incorporate domain knowledge of the biomedical entities (such as drug, disease, genes), obtained from Knowledge Gr(...) | Scopus | 2021 | - | Mondal I. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103121791&partnerID=40&md5=2d3c79babc4ad8241255205756899bff | India | augmented language models | validation research | technique | health |
| Conference Paper | Bio-Soda: Enabling Natural Language Question Answering over Knowledge Graphs without Training Data | Knowledge Graphs; Question Answering; Ranking(...) | The problem of natural language processing over structured data has become a growing research field, both within the relational database and the Semantic Web community, with significant efforts involved in question answering over knowledge graphs (KGQA). However, many of these approaches are either specifically targeted at open-domain question answering using DBpedia, or require large training datasets to translate a natural language question to SPARQL in order to query the knowledge graph. Henc(...) | ACM | 2021 | 10.1145/3468791.3469119 | Sima A.C., Mendes De Farias T., Anisimova M., Dessimoz C., Robinson-Rechavi M., Zbinden E., Stockinger K. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112749774&doi=10.1145%2f3468791.3469119&partnerID=40&md5=7dd1a4f7df7992cd50f585e8f4e09851 | Switzerland, United Kingdom | question answering | validation research | technique | - |
| Conference Paper | Building a Knowledge Graph of Vietnam Tourism from Text | Co-reference resolution; Google search; Knowledge graph; Natural language processing; Triples extraction(...) | Most data in the world is in form of text. Therefore, we can say text stores large amount of the knowledge of human beings. Extracting useful knowledge from text, however, is not a simple task. In this paper, we present a complete pipeline to extract knowledge from paragraph. This pipeline combines state-of-the-art systems in order to yield optimal results. There are some other Knowledge Graphs such as Google Knowledge Graph, YAGO, or DBpedia. Most of the data in these Knowledge Graphs is in Eng(...) | Scopus | 2021 | 10.1007/978-981-33-4069-5_1 | Do P., Le H. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103480870&doi=10.1007%2f978-981-33-4069-5_1&partnerID=40&md5=a009d3025a874d1a03d5478e9ec09636 | Vietnam | entity extraction, relation extraction, entity classification | solution proposal | method | tourism |
| Conference Paper | Cbench: Demonstrating Comprehensive Evaluation of Questianswering Systems over Knowledge Graphs through Deep Analysis of Benchmarks | Knowledge graph; Natural language processing systems; Structural properties; Syntactics; Comprehensive evaluation; Excel; Fine grained; Knowledge graphs; Natural language questions; Property; Question answering systems; Benchmarking(...) | A plethora of question answering (QA) systems that retrieve answers to natural language questions from knowledge graphs have been developed in recent years. However, choosing a benchmark to accurately assess the quality of a question answering system is a challenging task due to the high degree of variations among the available benchmarks with respect to their fine-grained properties. In this demonstration, we introduce CBench, an extensible, and more informative benchmarking suite for analyzing(...) | Scopus | 2021 | 10.14778/3476311.3476326 | Orogat A., El-Roby A. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119964973&doi=10.14778%2f3476311.3476326&partnerID=40&md5=fc7681a9f7ee0026a1f009cff40527ed | Canada | question answering | solution proposal | tool | - |
| Conference Paper | Cbench: Towards Better Evaluation of Question Answering Knowledge Graphs | Artificial intelligence; Benchmarking; Graphic methods; Natural language processing systems; Quality control; Query languages; Query processing; Structural properties; Syntactics; Expert users; Fine grained; Knowledge graphs; Natural languages; Property; QA system; Question Answering; Question answering systems; Structured queries; Structured Query Language; Knowledge graph(...) | Recently, there has been an increase in the number of knowledge graphs that can be only queried by experts. However, describing questions using structured queries is not straightforward for non-expert users who need to have sufficient knowledge about both the vocabulary and the structure of the queried knowledge graph, as well as the syntax of the structured query language used to describe the user’s information needs. The most popular approach introduced to overcome the aforementioned challenge(...) | Scopus | 2021 | 10.14778/3457390.3457398 | Orogat A., Liu I., El-Roby A. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115299221&doi=10.14778%2f3457390.3457398&partnerID=40&md5=166f214eb8cc635f55359211a1100b16 | Canada | question answering | validation research | tool; resource | - |
| Conference Paper | Chinese Verb-Object Collocation Knowledge Graph Construction and Application | Ontology construction; Semantic relational framework; Verb-object collocation extraction; Verb-Object Collocation Knowledge Graph(...) | Verb is the core of a sentence. It can not only reflect the syntactic structure and semantic framework of the whole sentence, but also restrict the nominal elements which co-exist with them. They play a significant role in sentence. Verb-Object Collocation has received more and more attention owing to its high frequency, complexity and flexibility of using. Domestic researches on verb object collocation mainly focus on automatic recognition and construction of corresponding collocation knowledge(...) | Scopus | 2021 | 10.1007/978-3-030-78615-1_19 | Zhao Y., Li Y., Shao Y. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112033341&doi=10.1007%2f978-3-030-78615-1_19&partnerID=40&md5=8071b36663a80aae8ee6ece7c2b1399e | China | entity extraction, relation extraction, ontology construction | solution proposal | method; resource | - |
| Conference Paper | Coco-Ex: a Tool for Linking Concepts from Texts to Conceptnet | Computational linguistics; Graph structures; Graph theory; Knowledge representation; ConceptNet; Extracting concept; Freeforms; Knowledge graphs; Natural language text; String matching; Data mining(...) | In this paper we present COCO-EX, a tool for Extracting Concepts from texts and linking them to the ConceptNet knowledge graph. COCO-EX extracts meaningful concepts from natural language texts and maps them to conjunct concept nodes in ConceptNet, utilizing the maximum of relational information stored in the ConceptNet knowledge graph. COCO-EX takes into account the challenging characteristics of ConceptNet, namely that - unlike conventional knowledge graphs - nodes are represented as non-canoni(...) | ACL | 2021 | - | Becker M., Korfhage K., Frank A. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107282443&partnerID=40&md5=bf9c125f24319e7264ffc336123d4791 | Germany | entity extraction, entity linking | validation research | tool | - |
| Journal Article | Cogcn: Combining Co-Attention with Graph Convolutional Network for Entity Linking with Knowledge Graphs | co-attention mechanism; entity linking; graph convolutional network; knowledge graphs(...) | Entity linking is a fundamental task in natural language processing. The task of entity linking with knowledge graphs aims at linking mentions in text to their correct entities in a knowledge graph like DBpedia or YAGO2. Most of existing methods rely on hand-designed features to model the contexts of mentions and entities, which are sparse and hard to calibrate. In this paper, we present a neural model that first combines co-attention mechanism with graph convolutional network for entity linking(...) | Scopus | 2021 | 10.1111/exsy.12606 | Jia N., Cheng X., Su S., Ding L. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089248180&doi=10.1111%2fexsy.12606&partnerID=40&md5=a1fae4689af755a2ea63a664542f010a | China | entity linking | validation research | technique | - |
| Journal Article | Cokebert: Contextual Knowledge Selection and Embedding Towards Enhanced Pre-Trained Language Models | Pre-trained language model, Knowledge graph, Entity typing, Relation classification(...) | Several recent efforts have been devoted to enhancing pre-trained language models (PLMs) by utilizing extra heterogeneous knowledge in knowledge graphs (KGs), and achieved consistent improvements on various knowledge-driven NLP tasks. However, most of these knowledge-enhanced PLMs embed static sub-graphs of KGs (“knowledge context”), regardless of that the knowledge required by PLMs may change dynamically according to specific text (“textual context”). In this paper, we propose a novel framework(...) | ScienceDirect | 2021 | 10.1016/j.aiopen.2021.06.004 | Yusheng Su and Xu Han and Zhengyan Zhang and Yankai Lin and Peng Li and Zhiyuan Liu and Jie Zhou and Maosong Sun | https://www.sciencedirect.com/science/article/pii/S2666651021000188 | China | augmented language models | validation research | technique | - |
| Journal Article | Combining Knowledge Graph and Word Embeddings for Spherical Topic Modeling | Analytical models; Data models; Integrated circuit modeling; Knowledge graph (KG) embedding; Mathematical models; Probabilistic logic; representation learning; Semantics; Task analysis; topic modeling; von Mises-Fisher (vMF) distribution; word embedding.(...) | Probabilistic topic models are considered as an effective framework for text analysis that uncovers the main topics in an unlabeled set of documents. However, the inferred topics by traditional topic models are often unclear and not easy to interpret because they do not account for semantic structures in language. Recently, a number of topic modeling approaches tend to leverage domain knowledge to enhance the quality of the learned topics, but they still assume a multinomial or Gaussian document(...) | IEEE | 2021 | 10.1109/tnnls.2021.3112045 | Ennajari H., Bouguila N., Bentahar J. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115694717&doi=10.1109%2fTNNLS.2021.3112045&partnerID=40&md5=5cf7516967e1be91eb22e3d31399d4de | Canada | text analysis | validation research | technique | - |
| Conference Paper | Compare to the Knowledge: Graph Neural Fake News Detection with External Knowledge | Computational linguistics; Directed graphs; Knowledge graph; Semantics; Comparison networks; End to end; External knowledge; Heterogeneous documents; Heterogeneous graph; Knowledge graphs; Linguistic features; Neural modelling; News content; Semantic features; Knowledge based systems(...) | Nowadays, fake news detection, which aims to verify whether a news document is trusted or fake, has become urgent and important. Most existing methods rely heavily on linguistic and semantic features from the news content, and fail to effectively exploit external knowledge which could help determine whether the news document is trusted. In this paper, we propose a novel end-to-end graph neural model called CompareNet, which compares the news to the knowledge base (KB) through entities for fake n(...) | ACL | 2021 | - | Hu L., Yang T., Zhang L., Zhong W., Tang D., Shi C., Duan N., Zhou M. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118918173&partnerID=40&md5=d45c2848d5b7bf759bf5812f6d2aabf0 | China | text analysis | validation research | technique | news |
| Conference Paper | Complex Question Answering on Knowledge Graphs Using Machine Translation and Multi-Task Learning | Computational linguistics; Computer aided language translation; Knowledge representation; Multi-task learning; Natural language processing systems; Complex questions; Experimental analysis; Industrial settings; Machine translations; Natural languages; Question Answering; Sequential manners; Traditional approaches; Learning systems(...) | Question answering (QA) over a knowledge graph (KG) is a task of answering a natural language (NL) query using the information stored in KG. In a real-world industrial setting, this involves addressing multiple challenges including entity linking, multi-hop reasoning over KG, etc. Traditional approaches handle these challenges in a modularized sequential manner where errors in one module lead to the accumulation of errors in downstream modules. Often these challenges are inter-related and the so(...) | ACL | 2021 | - | Srivastava S., Patidar M., Chowdhury S., Agarwal P., Bhattacharya I., Shroff G. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107260439&partnerID=40&md5=c373d59afaff7a0f2c643c3999a4ce8c | India | question answering, machine translation | validation research | technique | - |
| Journal Article | Constructing Knowledge Graph with Public Resumes | Characters Knowledge Graph; Knowledge Graph; NER; Rusume Analyse(...) | [Objective] This paper constructs knowledge graph based on the public resume data with natural language processing technology, which provides new tool for traditional data analysis. [Context] The proposed method could automatically extract profesional backgrounds and job information from resumes, and then obtain the relationship of working experience and colleagues in the organizations. The visualized knowledge graph could provide decision support for talent selection, personnel appointment and (...) | Scopus | 2021 | 10.11925/infotech.2096-3467.2021.0145 | Kejie S., Huanting H., Bolin H. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115321022&doi=10.11925%2finfotech.2096-3467.2021.0145&partnerID=40&md5=fac212c8e1382c4b3b5ef733349723e6 | China | entity extraction, relation extraction, entity linking | solution proposal | tool | business |
| Conference Paper | Constructing Micro Knowledge Graphs from Technical Support Documents | Natural language processing systems; Search engines; Websites; Chatbots; Graph search; Key actions; Key entity; Knowledge graphs; Knowledge sources; Large corpora; Question answering systems; Technical support; Web-page; Knowledge graph(...) | Short technical support pages such as IBM Technotes are quite common in technical support domain. These pages can be very useful as the knowledge sources for technical support applications such as chatbots, search engines and question-answering (QA) systems. Information extracted from documents to drive technical support applications is often stored in the form of Knowledge Graph (KG). Building KGs from a large corpus of documents poses a challenge of granularity because a large number of entiti(...) | Scopus | 2021 | 10.1007/978-3-030-80418-3_37 | Kumar A., Gupta N., Dana S. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115852519&doi=10.1007%2f978-3-030-80418-3_37&partnerID=40&md5=071e55856bb7c1c89e6a838bad66b920 | India | entity extraction, relation extraction, entity linking | solution proposal | method | information technology |
| Conference Paper | Construction and Application of Knowledge Graph of Domestic Operating System Testing | Ontology construction, Reuse of test cases, Domestic operating system, Knowledge graph, Software testing(...) | Aiming at the problems of poor reusability of domestic operating system test cases and insufficient sharing of test case design experience at this stage, a method for constructing knowledge graphs in the field of domestic operating system testing is proposed, and ontology construction and natural language processing technologies are applied to the field of software testing. Use the strong correlation of the knowledge graph to mine the experience knowledge in the design of historical test cases, (...) | ACM | 2021 | 10.1145/3494885.3494933 | Jin, Dongsheng and Wang, Zhi and Li, Mingyang and Zhu, Xinjie | https://doi.org/10.1145/3494885.3494933 | China | entity extraction, relation extraction, ontology construction | solution proposal | tool | engineering |
| Conference Paper | Construction of Diabetes Knowledge Graph Based on Deep Learning | named entity recognition;relation extraction;knowledge extraction(...) | To integrate medical data which is scattered over the internet, natural language processing (NLP) is widely used in medical text mining. BERT (Bidirectional Encoder Representations from Transformers) is outstanding among many other representation models and vector representation based on Bert pre-training language model can help the target task learn more semantic information. The knowledge graph intuitively reveals the relationship between entities and helps explore deeper semantic connections (...) | IEEE | 2021 | 10.1109/icnisc54316.2021.00181 | Y. Lu; R. Zhao; S. Huang; R. Liu | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9603816 | China | entity extraction, relation extraction | validation research | tool | health |
| Conference Paper | Construction of Therapy-Disease Knowledge Graph (Tdkg) Based on Entity Relationship Extraction | Knowledge Graph; Natural Language Processing; Relation Extraction; Treatment(...) | The knowledge graph of treatment-disease relationship can be a benefit not only to understand, inquire, and learn the relations between treatments and diseases from a macro level, but also to obtain the differences between treatments to the same disease through the comparison of different treatments; with the aid of commonalities of some treatments, a treatment to a disease that has not been discovered may be recognized; and with the aid of the commonalities of some diseases, a treatment to a di(...) | IEEE | 2021 | 10.1109/aemcse51986.2021.00173 | Wang H., Wang A., Su F., Feng H., Chen Y. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114034422&doi=10.1109%2fAEMCSE51986.2021.00173&partnerID=40&md5=5680b3aa153414fa30059605ccad5812 | China | relation extraction | solution proposal | technique | health |
| Conference Paper | Contextual Language Models for Knowledge Graph Completion | GPT-2; Knowledge graph embedding; Triple classification(...) | Knowledge Graphs (KGs) have become the backbone of various machine learning based applications over the past decade. However, the KGs are often incomplete and inconsistent. Several representation learning based approaches have been introduced to complete the missing information in KGs. Besides, Neural Language Models (NLMs) have gained huge momentum in NLP applications. However, exploiting the contextual NLMs to tackle the Knowledge Graph Completion (KGC) task is still an open research problem. (...) | Scopus | 2021 | - | Biswas R., Sofronova R., Alam M., Sack H. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119402984&partnerID=40&md5=da78ec552b908dc18a2356ad70666ff0 | Germany | triple classification, augmented language models, knowledge graph embedding | validation research | technique | - |
| Journal Article | Contextualized Knowledge-Aware Attentive Neural Network: Enhancing Answer Selection with Knowledge | knowledge graph, Answer selection, attention mechanism, graph convolutional network(...) | Answer selection, which is involved in many natural language processing applications, such as dialog systems and question answering (QA), is an important yet challenging task in practice, since conventional methods typically suffer from the issues of ignoring diverse real-world background knowledge. In this article, we extensively investigate approaches to enhancing the answer selection model with external knowledge from knowledge graph (KG). First, we present a context-knowledge interaction lea(...) | ACM | 2021 | 10.1145/3457533 | Deng, Yang and Xie, Yuexiang and Li, Yaliang and Yang, Min and Lam, Wai and Shen, Ying | https://doi.org/10.1145/3457533 | China, United States | question answering | validation research | technique | - |
| Journal Article | Conversation Concepts: Understanding Topics and Building Taxonomies for Financial Services | Financial services; FinTech; Knowledge graphs; Natural language processing; Relation extraction; Taxonomies; Term extraction(...) | Knowledge graphs are proving to be an increasingly important part of modern enterprises, and new applications of such enterprise knowledge graphs are still being found. In this paper, we report on the experience with the use of an automatic knowledge graph system called Saffron in the context of a large financial enterprise and show how this has found applications within this enterprise as part of the “Conversation Concepts Artificial Intelligence” tool. In particular, we analyse the use cases f(...) | Scopus | 2021 | 10.3390/info12040160 | McCrae J.P., Mohanty P., Narayanan S., Pereira B., Buitelaar P., Karmakar S., Sarkar R. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105002983&doi=10.3390%2finfo12040160&partnerID=40&md5=d1f30ceca340849e8c78ea0b669768c3 | Ireland, United States | conversational interfaces, semantic search | evaluation research | tool | business |
| Conference Paper | Conversational Question Answering over Knowledge Graphs with Transformer and Graph Attention Networks | Computational linguistics; Knowledge representation; Semantics; Attention model; Entity recognition; Knowledge graphs; Logical forms; Question Answering; Semantic parsing; State of the art; Transformer modeling; Complex networks(...) | This paper addresses the task of (complex) conversational question answering over a knowledge graph. For this task, we propose LASAGNE (muLti-task semAntic parSing with trAnsformer and Graph atteNtion nEtworks). It is the first approach, which employs a transformer architecture extended with Graph Attention Networks for multi-task neural semantic parsing. LASAGNE uses a transformer model for generating the base logical forms, while the Graph Attention model is used to exploit correlations betwee(...) | ACL | 2021 | - | Kacupaj E., Plepi J., Singh K., Thakkar H., Lehmann J., Maleshkova M. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107293854&partnerID=40&md5=815093af9a707bd0294af27c66c66de1 | Germany | conversational interfaces, question answering, augmented language models | validation research | technique | - |
| Conference Paper | Conversational Recommender System Based on Gru-Attention Neural Network | Knowledge graph;Conversational recommender system Deep learning;Neural network(...) | In recent years, the conversational recommender system (CRS) based on natural language processing technology has gained widespread attention, aiming to learn and model user preferences through interactive dialogue. Although existing research has improved the accuracy of the dialogue recommendation system to a certain extent, there are still some shortcomings that make it easy to generate more general and popular responses. This paper proposes a deep learning framework with GRU and attention mech(...) | IEEE | 2021 | 10.1109/icdsca53499.2021.9650212 | X. Wang; J. Wang; J. Liu | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9650212 | China | conversational interfaces | validation research | technique | - |
| Conference Paper | Cori: Collective Relation Integration with Data Augmentation for Open Information Extraction | Computational linguistics; Data integration; Forecasting; Integration; Open Data; Data augmentation; Free texts; Integration models; Knowledge graphs; Object extraction; Question Answering; Knowledge graph(...) | Integrating extracted knowledge from the Web to knowledge graphs (KGs) can facilitate tasks like question answering. We study relation integration that aims to align free-text relations in subject-relation-object extractions to relations in a target KG. To address the challenge that free-text relations are ambiguous, previous methods exploit neighbor entities and relations for additional context. However, the predictions are made independently, which can be mutually inconsistent. We propose a tw(...) | ACL | 2021 | - | Jiang Z., Han J., Sisman B., Dong X.L. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118924156&partnerID=40&md5=52901fd9d1dbc55fe9040f4e8adcee72 | United States | entity extraction, relation extraction | validation research | technique | - |
| Conference Paper | Cost-Effective Knowledge Graph Reasoning for Complex Factoid Questions | Factoid Question Answering; Knowledge Graph; Reasoning(...) | The task of reasoning over knowledge graph for factoid questions has received significant interest from the research community of natural language processing. Performing this task inevitably faces the issues of question complexity and reasoning efficiency. In this paper, we investigate modern reasoning approaches over knowledge graph to tackle complex factoid questions of diverse reasoning schemas with attractive speedup in computational efficiency. To this end, we propose two evidence retrieval(...) | IEEE | 2021 | 10.1109/ijcnn52387.2021.9533753 | Yang X., Chiang M.-F., Lee W.-C., Chang Y. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116430716&doi=10.1109%2fIJCNN52387.2021.9533753&partnerID=40&md5=dca9c8e035cec9968d2ecb18867c18ff | China, New Zealand, United States | question answering | validation research | method | - |
| Conference Paper | Cross-Domain Knowledge Discovery Based on Knowledge Graph and Patent Mining | Cross-Domain; Knowledge Graph; Natural Language Process (NLP); Patent Mining(...) | This paper studies an approach on cross-domain knowledge discovery to assist the conceptual stage of the design process related to mechanical engineering. Variable methods and tools are proposed to obtain knowledge within a given domain until now. However, methods on cross-domain knowledge analysis is under-developed. In this paper, domain knowledge graph is built automatically by employing natural language process (NLP) and patent mining. They comprise patent documents obtaining and knowledge e(...) | Scopus | 2021 | 10.1088/1742-6596/1744/4/042155 | Ye F., Fu T., Gong L., Gao J. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102170953&doi=10.1088%2f1742-6596%2f1744%2f4%2f042155&partnerID=40&md5=416fe466f6049e5cb4c4502d6f358877 | China | entity extraction | solution proposal | method | engineering |
| Conference Paper | Cross-Lingual Entity Alignment with Incidental Supervision | Alignment; Computational linguistics; Iterative methods; Knowledge representation; Learning systems; Benchmark datasets; Embedding method; Knowledge graphs; Learning process; Monolingual texts; Real-world objects; Research efforts; State-of-the-art methods; Embeddings(...) | Much research effort has been put to multilingual knowledge graph (KG) embedding methods to address the entity alignment task, which seeks to match entities in different language-specific KGs that refer to the same real-world object. Such methods are often hindered by the insufficiency of seed alignment provided between KGs. Therefore, we propose an incidentally supervised model, JEANS, which jointly represents multilingual KGs and text corpora in a shared embedding scheme, and seeks to improve (...) | ACL | 2021 | - | Chen M., Shi W., Zhou B., Roth D. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106367685&partnerID=40&md5=6ce87ce9d8dab67a1d80ecba9685f668 | United States | entity alignment, knowledge graph embedding | validation research | technique | - |
| Conference Paper | Deep Learning for Knowledge Graph Completion with Xlnet | GRU; KG Completion; Knowledge Graph; LSTM; XLNet(...) | Knowledge Graph is a graph knowledge base composed of fact entities and relations. Recently, the adoption of Knowledge Graph in Natural Language Processing tasks has proved the efficiency and convenience of KG. Therefore, the plausibility of Knowledge Graph become an import subject, which is also named as KG Completion or Link Prediction. The plausibility of Knowledge Graph reflects in the validness of triples which is structured representation of the entities and relations of Knowledge Graph. S(...) | ACM | 2021 | 10.1145/3480001.3480022 | Su M., Su H., Zheng H., Yan B. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119835760&doi=10.1145%2f3480001.3480022&partnerID=40&md5=2eb410cadcee0ff9cd2a634ed98a016b | China | error detection, link prediction | validation research | technique | - |
| Journal Article | Detecting Suicide Risk Using Knowledge-Aware Natural Language Processing and Counseling Service Data | Artificial intelligence; Knowledge graph; Natural language processing; Online counseling services; Suicide prevention(...) | Rationale: Detecting users at risk of suicide in text-based counseling services is essential to ensure that at-risk individuals are flagged and prioritized. Objective: The objective of this study is to develop a domain knowledge-aware risk assessment (KARA) model to improve our ability of suicide detection in online counseling systems. Methods: We obtained the largest known de-identified dataset from an emotional support system established in Hong Kong, comprising 5682 Cantonese conversations be(...) | ScienceDirect | 2021 | 10.1016/j.socscimed.2021.114176 | Xu Z., Xu Y., Cheung F., Cheng M., Lung D., Law Y.W., Chiang B., Zhang Q., Yip P.S.F. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108917661&doi=10.1016%2fj.socscimed.2021.114176&partnerID=40&md5=90af0d089719b25e3fe12732734fa4a1 | Hong Kong | text classification | validation research | method | health |
| Journal Article | Developing a Vietnamese Tourism Question Answering System Using Knowledge Graph and Deep Learning | Question answering system; Knowledge graph; Natural language processing; Deep learning; Graph query; Vietnamese tourism(...) | In recent years, Question Answering (QA) systems have increasingly become very popular in many sectors. This study aims to use a knowledge graph and deep learning to develop a QA system for tourism in Vietnam. First, the QA system replies to a user's question about a place in Vietnam. Then, the QA describes it in detail such as when the place was discovered, why the place's name was called like that, and so on. Finally, the system recommends some related tourist attractions to users. Meanwhile, (...) | WoS | 2021 | 10.1145/3453651 | Do P,Phan V TH,Gupta BB | http://dx.doi.org/10.1145/3453651 | India | question answering | solution proposal | tool | tourism |
| Conference Paper | Do Judge an Entity by Its Name Entity Typing Using Language Models | Deep neural networks; Entity type prediction; Knowledge graph completion(...) | The entity type information in a Knowledge Graph (KG) plays an important role in a wide range of applications in Natural Language Processing such as entity linking, question answering, relation extraction, etc. However, the available entity types are often noisy and incomplete. Entity Typing is a non-trivial task if enough information is not available for the entities in a KG. In this work, neural language models and a character embedding model are exploited to predict the type of an entity from(...) | Scopus | 2021 | 10.1007/978-3-030-80418-3_12 | Biswas R., Sofronova R., Alam M., Heist N., Paulheim H., Sack H. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115845674&doi=10.1007%2f978-3-030-80418-3_12&partnerID=40&md5=d4b7cc03fa214a9c6a9b54d6d76ee668 | Germany | entity classification | validation research | guidelines | - |
| Conference Paper | Dozen: Cross-Domain Zero Shot Named Entity Recognition with Knowledge Graph | cross-domain machine learning; knowledge graph; named entity recognition; natural language processing; zero-shot learning(...) | With the new developments of natural language processing, increasing attention has been given to the task of Named Entity Recognition (NER). However, the vast majority of work focus on a small number of large-scale annotated datasets with a limited number of entities such as person, location and organization. While other datasets have been introduced with domain-specific entities, the smaller size of these largely limits the applicability of state-of-the-art deep models. Even if there are promis(...) | Scopus | 2021 | 10.1145/3404835.3463113 | Nguyen H.-V., Gelli F., Poria S. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111631359&doi=10.1145%2f3404835.3463113&partnerID=40&md5=13d14a306f649d107b8a45a11edecc1b | Singapore | entity extraction | validation research | technique | - |
| Journal Article | Drug Repositioning Based on Network-Specific Core Genes Identifies Potential Drugs for the Treatment of Autism Spectrum Disorder in Children | Autism spectrum disorder; Coexpression network; Drug repositioning; Knowledge graph; Natural language processing(...) | Identification of exact causative genes is important for in silico drug repositioning based on drug-gene-disease relationships. However, the complex polygenic etiology of the autism spectrum disorder (ASD) is a challenge in the identification of etiological genes. The network-based core gene identification method can effectively use the interactions between genes and accurately identify the pathogenic genes of ASD. We developed a novel network-based drug repositioning framework that contains thr(...) | ScienceDirect | 2021 | 10.1016/j.csbj.2021.06.046 | Gao H., Ni Y., Mo X., Li D., Teng S., Huang Q., Huang S., Liu G., Zhang S., Tang Y., Lu L., Liang H. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110120513&doi=10.1016%2fj.csbj.2021.06.046&partnerID=40&md5=121efe941832da8b55c21e42521bcd11 | China | semantic search | validation research | method | health |
| Journal Article | Drug-Drug Interaction Predictions Via Knowledge Graph and Text Embedding: Instrument Validation Study | Drug-drug interactions; Knowledge graph; Natural language processing(...) | Background: Minimizing adverse reactions caused by drug-drug interactions (DDIs) has always been a prominent research topic in clinical pharmacology. Detecting all possible interactions through clinical studies before a drug is released to the market is a demanding task. The power of big data is opening up new approaches to discovering various DDIs. However, these data contain a huge amount of noise and provide knowledge bases that are far from being complete or used with reliability. Most exist(...) | Scopus | 2021 | 10.2196/28277 | Wang M., Wang H., Liu X., Ma X., Wang B. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108820673&doi=10.2196%2f28277&partnerID=40&md5=12ad88a4b49d95f0f4fceb820bf885ca | China | entity linking, link prediction, knowledge graph embedding | validation research | method | health |
| Conference Paper | Dynamic Causality Knowledge Graph Generation for Supporting the Chatbot Healthcare System | Artificial intelligent; Causality analysis; Chatbot; Healthcare; Knowledge graph; Natural language processing(...) | With recent viruses across the world affecting millions and millions of people, the self-healthcare information systems show an important role in helping individuals to understand the risks, self-assessment, and self-educating to avoid being affected. In addition, self-healthcare information systems can perform more interactive tasks to effectively assist the treatment process and health condition management. Currently, the technologies used in such kind of systems are mostly based on text crawl(...) | Scopus | 2021 | 10.1007/978-3-030-63092-8_3 | Yu H.Q. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096464640&doi=10.1007%2f978-3-030-63092-8_3&partnerID=40&md5=ec3417be4502dd39e391b41896dfbebb | United Kingdom | conversational interfaces | solution proposal | method | health |
| Conference Paper | Employing Argumentation Knowledge Graphs for Neural Argument Generation | Computational linguistics; Encoding (symbols); Graphic methods; Search engines; Downstream applications; High quality; Knowledge graphs; Text generations; Wikipedia; Knowledge graph(...) | Generating high-quality arguments, while being challenging, may benefit a wide range of downstream applications, such as writing assistants and argument search engines. Motivated by the effectiveness of utilizing knowledge graphs for supporting general text generation tasks, this paper investigates the usage of argumentation-related knowledge graphs to control the generation of arguments. In particular, we construct and populate three knowledge graphs, employing several compositions of them to e(...) | ACL | 2021 | - | Al-Khatib K., Trautner L., Wachsmuth H., Hou Y., Stein B. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118945370&partnerID=40&md5=e5502312ee82e85a5b249ec70dca3fb5 | Germany, Ireland | text generation | validation research | method | - |
| Conference Paper | Enabling Language Representation with Knowledge Graph and Structured Semantic Information | knowledge graph; language model; semantic information(...) | Pre-trained language models have been widely recognized and applied. While common pre-training language representation models(PLMs) usually focus on grasping the co-occurrence of words or sentences in simple tasks, more and more researchers realize that external information, i.e., knowledge graph (KG) and clear structured semantics, can be vital in natural language understanding tasks. Therefore, using external information to enhance PLMs (such as BERT) has gradually become a popular direction. (...) | IEEE | 2021 | 10.1109/ccai50917.2021.9447453 | Xu W., Fang M., Yang L., Jiang H., Liang G., Zuo C. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111426193&doi=10.1109%2fCCAI50917.2021.9447453&partnerID=40&md5=a69b1fc7624afe01cfd28fd4e42465dc | China | augmented language models | validation research | technique | - |
| Conference Paper | End-To-End Construction of Nlp Knowledge Graph | - | - | ACL | 2021 | 10.18653/v1/2021.findings-acl.165 | Mondal, Ishani and Hou, Yufang and Jochim, Charles | https://aclanthology.org/2021.findings-acl.165 | India, Ireland | relation extraction, | validation research | method | scholarly domain |
| Conference Paper | Entity Classification for Military Knowledge Graph Based on Baidu Encyclopedia Distance Learning | Distance learning; Entity classification; Military industry knowledge graph; Web crawler(...) | Entity types are a critical enabler for many NLP tasks that use KGs as a reference source. However, Classifying terminological entities without context remains an important outstanding obstacle in the field of KG completion. In this paper, we put forward a method combining distance learning and deep learning to address the classification of entity with no context. We compare the performance of our method with several text classification methods and shows our approach is empirically effective. Fu(...) | IEEE | 2021 | 10.1109/ibcast51254.2021.9393163 | Jia H., Li Y., Song D., Wang Q. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104659784&doi=10.1109%2fIBCAST51254.2021.9393163&partnerID=40&md5=67d8376aaf5f2c3d6e77333ea73be386 | China | entity classification, entity extraction | validation research | technique | public sector |
| Conference Paper | Entity Pair Recognition Using Semantic Enrichment and Adversarial Training for Chinese Drug Knowledge Extraction | medical field, knowledge induction, subclass and hyponym, entity pair verification(...) | Existing knowledge extraction methods in pharmacy often use natural language processing tools and deep learning model to identify drug entities and extract their relationships from drug instructions, thus obtaining drug-drug or drug-disease knowledge. However, sentences in drug instructions may contain multiple drug-related entities, and existing methods lack the capability of identifying valid the "drug-drug" or "drug-disease" entity pairs. This will introduce significant noise data in the subs(...) | ACM | 2021 | 10.1145/3500931.3500939 | Gao, Feng and Zhou, LunSheng and Gu, JinGuang | https://doi.org/10.1145/3500931.3500939 | China | entity extraction, relation extraction | validation research | technique | health |
| Conference Paper | Entity-Based Knowledge Graph Information Retrieval for Biomedical Articles | BERT; Entity recognition; Knowledge graph; Natural language processing(...) | In this paper, we present an information retrieval system on a corpus of scientific articles related to COVID-19 and biomedical. We build a heterogeneous entity-based knowledge graph network, where edges are shared between biomedical entities and paper names, where entities appear in abstract of the paper. The biomedical entities are derived from the abstract of the scientific articles using a fine-tuned Bio-BERT model. For a user query, entities are derived using a fine-tuned Bio-BERT model and(...) | Scopus | 2021 | 10.1007/978-981-16-1089-9_62 | Prasad V.K., Bharti S., Koganti N. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111957442&doi=10.1007%2f978-981-16-1089-9_62&partnerID=40&md5=f8c07eceb3d54c4596d7a4a702961339 | India | semantic search | solution proposal | tool | health; scholarly domain |
| Journal Article | Entity-Centric Fully Connected Gcn for Relation Classification | Graph convolutional network; Natural language processing; Relation classification(...) | Relation classification is an important task in the field of natural language processing, and it is one of the important steps in constructing a knowledge graph, which can greatly reduce the cost of constructing a knowledge graph. The Graph Convolutional Network (GCN) is an effective model for accurate relation classification, which models the dependency tree of textual instances to extract the semantic features of relation mentions. Previous GCN based methods treat each node equally. However, t(...) | Scopus | 2021 | 10.3390/app11041377 | Long J., Wang Y., Wei X., Ding Z., Qi Q., Xie F., Qian Z., Huang W. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100688691&doi=10.3390%2fapp11041377&partnerID=40&md5=f1e0a3b96549637d36bdfb20bfb890a9 | China | relation classification | validation research | technique | - |
| Conference Paper | Esra: Explainable Scientific Research Assistant | Computational linguistics; Paper; Graph visualization; Knowledge graphs; Literature search; Query visualizations; Related entities; Scientific researches; Search process; Search system; WEB application; Web applications; Knowledge graph(...) | We introduce Explainable Scientific Research Assistant (ESRA), a literature discovery platform that augments search results with relevant details and explanations, aiding users in understanding more about their queries and the returned papers beyond existing literature search systems. Enabled by a knowledge graph we extracted from abstracts of 23k papers on the arXiv’s cs.CL category, ESRA provides three main features: explanation (for why a paper is returned to the user), list of facts (that ar(...) | ACL | 2021 | - | Hongwimol P., Kehasukcharoen P., Laohawarutchai P., Lertvittayakumjorn P., Ng A.B., Lai Z., Liu T., Vateekul P. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118942127&partnerID=40&md5=b67cdbd3d46527806d507dbc19ad92bf | United Kingdom, Thailand, United States | entity extraction, relation extraction, semantic search | validation research | tool | scholarly domain |
| Conference Paper | Explainable Zero-Shot Topic Extraction Using a Common-Sense Knowledge Graph | Explainable NLP; Knowledge graph; Topic extraction; Zero-shot classification(...) | Pre-trained word embeddings constitute an essential building block for many NLP systems and applications, notably when labeled data is scarce. However, since they compress word meanings into a fixed-dimensional representation, their use usually lack interpretability beyond a measure of similarity and linear analogies that do not always reflect real-world word relatedness, which can be important for many NLP applications. In this paper, we propose a model which extracts topics from text documents(...) | Scopus | 2021 | 10.4230/oasics.ldk.2021.17 | Harrando I., Troncy R. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115072891&doi=10.4230%2fOASIcs.LDK.2021.17&partnerID=40&md5=b88159d302a5184b02f48e0d8fac176d | France | text classification | validation research | tool | - |
| Journal Article | Exploiting Non-Taxonomic Relations for Measuring Semantic Similarity and Relatedness in Wordnet | Information content; Knowledge graph; Semantic similarity and relatedness; WordNet(...) | Various applications in computational linguistics and artificial intelligence employ semantic similarity to solve challenging tasks, such as word sense disambiguation, text classification, information retrieval, machine translation, and document clustering. To our knowledge, research to date rely solely on the taxonomic relation “ISA” to evaluate semantic similarity and relatedness between terms. This paper explores the benefits of using all types of non-taxonomic relations in large linked data,(...) | ScienceDirect | 2021 | 10.1016/j.knosys.2020.106565 | AlMousa M., Benlamri R., Khoury R. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095852763&doi=10.1016%2fj.knosys.2020.106565&partnerID=40&md5=5573a5a128dd93ea1e4d6a4101770633 | Canada | semantic similarity | validation research | technique | - |
| Conference Paper | Exploring Sentence Embedding Structures for Semantic Relation Extraction | knowledge graph embedding; semantic relation extraction; sentence embeddings(...) | Sentence embeddings encode natural language sentences as low-dimensional, dense vectors and have improved NLP tasks, including relation extraction, which aims at identifying structured relations defined in a knowledge base from unstructured text. A promising and more efficient approach would be to embed both the text and structured knowledge in low-dimensional spaces and discover alignments between them. We develop such an alignment procedure and evaluate the extent to which sentences carrying s(...) | IEEE | 2021 | 10.1109/ijcnn52387.2021.9534215 | Kalinowski A., An Y. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116419054&doi=10.1109%2fIJCNN52387.2021.9534215&partnerID=40&md5=3d43052877a8b6499c13a4ac4d25bed1 | United States | relation extraction | validation research | technique | - |
| Conference Paper | Extracting Relations in Texts with Concepts of Neighbours | Deep learning; Information analysis; Knowledge representation; Learning systems; Natural language processing systems; Syntactics; Human interactions; Knowledge graphs; Learning methods; Named entities; NAtural language processing; Relation extraction; State-of-the-art performance; Syntactic structure; Formal concept analysis(...) | During the last decade, the need for reliable and massive Knowledge Graphs (KG) increased. KGs can be created in several ways: manually with forms or automatically with Information Extraction (IE), a natural language processing task for extracting knowledge from text. Relation Extraction is the part of IE that focuses on identifying relations between named entities in texts, which amounts to find new edges in a KG. Most recent approaches rely on deep learning, achieving state-of-the-art performa(...) | Scopus | 2021 | 10.1007/978-3-030-77867-5_10 | Ayats H., Cellier P., Ferré S. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111431238&doi=10.1007%2f978-3-030-77867-5_10&partnerID=40&md5=ce822622c48091c74f8d2d72475eb3f6 | France | relation extraction | validation research | technique | - |
| Journal Article | Farsbase-Kbp: a Knowledge Base Population System for the Persian Knowledge Graph | Canonicalization; Knowledge extraction; Knowledge Graph; Natural Language Processing; Persian language(...) | While most of the knowledge bases already support the English language, there is only one knowledge base for the Persian language, known as FarsBase, which is automatically created via semi-structured web information. Unlike English knowledge bases such as Wikidata, which have tremendous community support, the population of a knowledge base like FarsBase must rely on automatically extracted knowledge. Knowledge base population can let FarsBase keep growing in size, as the system continues workin(...) | ScienceDirect | 2021 | 10.1016/j.websem.2021.100638 | Asgari-Bidhendi M., Janfada B., Minaei-Bidgoli B. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103692386&doi=10.1016%2fj.websem.2021.100638&partnerID=40&md5=6f45b35d1f537ef77283ea160e9c010e | Iran | entity linking, entity extraction, relation extraction | solution proposal | tool; resource | - |
| Journal Article | Fine-Grained Evaluation of Knowledge Graph Embedding Model in Knowledge Enhancement Downstream Tasks | Embedding model; Evaluation; Knowledge graph(...) | Knowledge graph (KG) embedding models are proposed to encode entities and relations into a low-dimensional vector space, in turn, can support various machine learning models on KG completion with good performance and robustness. However, the current entity ranking protocol about KG completion cannot adequately evaluate the impacts of KG embedding models in real-world applications. However, KG embeddings are not widely used as word embeddings. An asserted powerful KG embedding model may not be ef(...) | ScienceDirect | 2021 | 10.1016/j.bdr.2021.100218 | Zhang Y., Li B., Gao H., Ji Y., Yang H., Wang M., Chen W. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102311321&doi=10.1016%2fj.bdr.2021.100218&partnerID=40&md5=02b03a3afd3100142fd90cff66c30475 | Australia, China | knowledge graph embedding | validation research | guidelines | - |
| Conference Paper | Fine-Grained Information Extraction from Biomedical Literature Based on Knowledge-Enriched Abstract Meaning Representation | Artificial intelligence; Computational linguistics; Information retrieval; Knowledge based systems; Knowledge graph; Natural language processing systems; Semantics; Background knowledge; Biomedical information extractions; Biomedical literature; Domain specific; External knowledge; Extraction modeling; Fine grained; Natural languages texts; Scientific literature; Scientific papers; Complex networks(...) | Biomedical Information Extraction from scientific literature presents two unique and nontrivial challenges. First, compared with general natural language texts, sentences from scientific papers usually possess wider contexts between knowledge elements. Moreover, comprehending the fine-grained scientific entities and events urgently requires domain-specific background knowledge. In this paper, we propose a novel biomedical Information Extraction (IE) model to tackle these two challenges and extra(...) | ACL | 2021 | - | Zhang Z., Parulian N., Ji H., Elsayed A.S., Myers S., Palmer M. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115867463&partnerID=40&md5=1589ab2a22a2d2f1b0cf316aabe44065 | United States | entity extraction, relation extraction | validation research | method | health |
| Journal Article | Generating Knowledge Graphs by Employing Natural Language Processing and Machine Learning Techniques Within the Scholarly Domain | Graphic methods; Hybrid systems; Knowledge representation; Machine learning; Text mining; Explicit representation; Machine learning methods; Machine learning techniques; NAtural language processing; Scientific knowledge; Scientific literature; Scientific researches; Technological infrastructure; Natural language processing systems(...) | The continuous growth of scientific literature brings innovations and, at the same time, raises new challenges. One of them is related to the fact that its analysis has become difficult due to the high volume of published papers for which manual effort for annotations and management is required. Novel technological infrastructures are needed to help researchers, research policy makers, and companies to time-efficiently browse, analyse, and forecast scientific research. Knowledge graphs i.e., lar(...) | ScienceDirect | 2021 | 10.1016/j.future.2020.10.026 | Dessì D., Osborne F., Reforgiato Recupero D., Buscaldi D., Motta E. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095915737&doi=10.1016%2fj.future.2020.10.026&partnerID=40&md5=453481b30edbb795ab1ee9b9f6564330 | Germany, France, United Kingdom, Italy | entity extraction, relation extraction | solution proposal | method | scholarly domain |
| Journal Article | Gis-Kg: Building a Large-Scale Hierarchical Knowledge Graph for Geographic Information Science | Geographic information science (GIS); information retrieval; knowledge graph; natural language processing; ontology(...) | An organized knowledge base can facilitate the exploration of existing knowledge and the detection of emerging topics in a domain. Knowledge about and around Geographic Information Science and its associated system technologies (GIS) is complex, extensive and emerging rapidly. Taking the challenge, we built a GIS knowledge graph (GIS-KG) by (1) merging existing GIS bodies of knowledge to create a hierarchical ontology and then (2) applying deep-learning methods to map GIS publications to the ont(...) | Scopus | 2021 | 10.1080/13658816.2021.2005795 | Du J., Wang S., Ye X., Sinton D.S., Kemp K. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119981012&doi=10.1080%2f13658816.2021.2005795&partnerID=40&md5=2cfe2e15000b6bf298f7e3a19e3d2df0 | United States | ontology construction, entity linking, entity alignment | validation research | method | natural science |
| Conference Paper | Gmh: a General Multi-Hop Reasoning Model for Kg Completion | - | Knowledge graphs are essential for numerous downstream natural language processing applications, but are typically incomplete with many facts missing. This results in research efforts on multi-hop reasoning task, which can be formulated as a search process and current models typically perform short distance reasoning. However, the long-distance reasoning is also vital with the ability to connect the superficially unrelated entities. To the best of our knowledge, there lacks a general framework t(...) | ACL | 2021 | 10.18653/v1/2021.emnlp-main.276 | Zhang, Yao and Liang, Hongru and Jatowt, Adam and Lei, Wenqiang and Wei, Xin and Jiang, Ning and Yang, Zhenglu | https://aclanthology.org/2021.emnlp-main.276 | Austria, China, Singapore | link prediction, relation classification | validation research | method | - |
| Conference Paper | Graph-Assisted Attention for Path Finding in Question Answering Task | Attention; bAbI dataset; Dynamic memory network; End to end memory network; Graph linearization; Knowledge graph; Path finding task; Question answering(...) | Attention-based memory networks, a class of deep learning algorithms in Natural Language Processing (NLP), capture long-range dependencies present in text data and is a popular recipe in currently available question answering (QA) systems. However, multi-hop QA systems pose additional challenges that these memory networks cannot comfortably handle with their attention spans. Path-finding tasks are a flavor of such multi-hop QA, and it does not have the additional complexity of implicit reasoning(...) | Scopus | 2021 | 10.1007/978-981-15-9774-9_68 | Guruprasad M., Agarwal J., Lokesh Kumar T.N., Das B. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109013013&doi=10.1007%2f978-981-15-9774-9_68&partnerID=40&md5=ea99cb535839584c8326f98fbfef9d17 | India | question answering | validation research | technique | - |
| Journal Article | Graph-Based Reasoning Model for Multiple Relation Extraction | Information extraction; Natural language processing; Neural networks; Relation extraction(...) | Linguistic knowledge is useful for various NLP tasks, but the difficulty lies in the representation and application. We consider that linguistic knowledge is implied in a large-scale corpus, while classification knowledge, the knowledge related to the definitions of entity and relation types, is implied in the labeled training data. Therefore, a corpus subgraph is proposed to mine more linguistic knowledge from the easily accessible unlabeled data, and sentence subgraphs are used to acquire clas(...) | ScienceDirect | 2021 | 10.1016/j.neucom.2020.09.025 | Huang H., Lei M., Feng C. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092253997&doi=10.1016%2fj.neucom.2020.09.025&partnerID=40&md5=9f9d541afc98a60e2a8c0c34dd11f21d | China | relation extraction | validation research | technique | - |
| Conference Paper | Graphhopper: Multi-Hop Scene Graph Reasoning for Visual Question Answering | Knowledge graph reasoning; Multi-modal reasoning; Reinforcement learning; Scene graph reasoning; Visual Question Answering (VQA)(...) | Visual Question Answering (VQA) is concerned with answering free-form questions about an image. Since it requires a deep semantic and linguistic understanding of the question and the ability to associate it with various objects that are present in the image, it is an ambitious task and requires multi-modal reasoning from both computer vision and natural language processing. We propose Graphhopper, a novel method that approaches the task by integrating knowledge graph reasoning, computer vision, (...) | Scopus | 2021 | 10.1007/978-3-030-88361-4_7 | Koner R., Li H., Hildebrandt M., Das D., Tresp V., Günnemann S. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116858375&doi=10.1007%2f978-3-030-88361-4_7&partnerID=40&md5=f0f5cc665dc9f46cc1c8a83af67176b1 | Germany | question answering | validation research | method | - |
| Journal Article | Hierarchical Concept-Driven Language Model | Language modeling, hierarchical language modeling, representation learning, interpretation, recurrent conceptualization-enhanced gamma belief network, concept semantic information, text generation(...) | For guiding natural language generation, many semantic-driven methods have been proposed. While clearly improving the performance of the end-to-end training task, these existing semantic-driven methods still have clear limitations: for example, (i) they only utilize shallow semantic signals (e.g., from topic models) with only a single stochastic hidden layer in their data generation process, which suffer easily from noise (especially adapted for short-text etc.) and lack of interpretation; (ii) (...) | ACM | 2021 | 10.1145/3451167 | Wang, Yashen and Zhang, Huanhuan and Liu, Zhirun and Zhou, Qiang | https://doi.org/10.1145/3451167 | China | augmented language models, text generation | validation research | technique | - |
| Conference Paper | Hornet: Enriching Pre-Trained Language Representations with Heterogeneous Knowledge Sources | heterogeneous graph attention network; knowledge graph; natural language processing; pre-trained language model(...) | Knowledge-Enhanced Pre-trained Language Models (KEPLMs) improve the language understanding abilities of deep language models by leveraging the rich semantic knowledge from knowledge graphs, other than plain pre-training texts. However, previous efforts mostly use homogeneous knowledge (especially structured relation triples in knowledge graphs) to enhance the context-aware representations of entity mentions, whose performance may be limited by the coverage of knowledge graphs. Also, it is unclea(...) | Scopus | 2021 | 10.1145/3459637.3482436 | Zhang T., Cai Z., Wang C., Li P., Li Y., Qiu M., Tang C., He X., Huang J. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119181897&doi=10.1145%2f3459637.3482436&partnerID=40&md5=04aecaaa3d768ded99a4db0e0108b8c9 | China | augmented language models, knowledge graph embedding | validation research | technique | - |
| Conference Paper | How Knowledge Graph and Attention Help a Quantitative Analysis into Bag-Level Relation Extraction | Computational linguistics; Extraction; Attention mechanisms; Distribution patterns; Knowledge graphs; Modeling abilities; Noise distribution; Performance; Qualitative analysis; Real-world datasets; Relation extraction; Supervised methods; Knowledge graph(...) | Knowledge Graph (KG) and attention mechanism have been demonstrated effective in introducing and selecting useful information for weakly supervised methods. However, only qualitative analysis and ablation study are provided as evidence. In this paper, we contribute a dataset and propose a paradigm to quantitatively evaluate the effect of attention and KG on bag-level relation extraction (RE). We find that (1) higher attention accuracy may lead to worse performance as it may harm the model's abil(...) | ACL | 2021 | - | Hu Z., Cao Y., Huang L., Chua T.-S. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118918083&partnerID=40&md5=1922ab36dc0690e7e80b7f56f3bf47f8 | Singapore, United States | relation extraction | validation research | resource; method | - |
| Conference Paper | Identify, Align, and Integrate: Matching Knowledge Graphs to Commonsense Reasoning Tasks | Computational linguistics; Integration; Commonsense reasoning; External knowledge; Human evaluation; Knowledge gaps; Knowledge graphs; Knowledge integration; Knowledge tasks; Peak performance; Knowledge representation(...) | Integrating external knowledge into commonsense reasoning tasks has shown progress in resolving some, but not all, knowledge gaps in these tasks. For knowledge integration to yield peak performance, it is critical to select a knowledge graph (KG) that is well-aligned with the given task's objective. We present an approach to assess how well a candidate KG can correctly identify and accurately fill in gaps of reasoning for a task, which we call KG-to-task match. We show this KG-to-task match in 3(...) | ACL | 2021 | - | Bauer L., Bansal M. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107268857&partnerID=40&md5=1a3d30a6d333c1d466373512665e1c17 | United States | augmented language models, natural language inference | validation research | method | - |
| Conference Paper | Identifying Used Methods and Datasets in Scientific Publications | Character recognition; Indexing (of information); Knowledge representation; Natural language processing systems; Human interactions; Identifying methods; Knowledge graphs; Named entity recognition; Paper recommendations; Scientific method; Scientific publications; Textual contexts; Publishing(...) | Although it has become common to assess publications and researchers by means of their citation count (e.g., using the h-index), measuring the impact of scientific methods and datasets (e.g., using an h-index for datasets) has been performed only to a limited extent. This is not surprising because the usage information of methods and datasets is typically not explicitly provided by the authors, but hidden in a publication's text. In this paper, we propose an approach to identifying methods and d(...) | Scopus | 2021 | - | Färber M., Albers A., Schüber F. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103121171&partnerID=40&md5=498e688252287f4372805218fbb9eeca | Germany | entity extraction, entity linking, semantic search | validation research | method | scholarly domain |
| Journal Article | Incorporating Domain Knowledge into Language Models by Using Graph Convolutional Networks for Assessing Semantic Textual Similarity: Model Development and Performance Comparison | Bidirectional encoder representation from transformers; Graph neural networks; National NLP Clinical Challenges; Natural language processing(...) | Background: Although electronic health record systems have facilitated clinical documentation in health care, they have also introduced new challenges, such as the proliferation of redundant information through the use of copy and paste commands or templates. One approach to trimming down bloated clinical documentation and improving clinical summarization is to identify highly similar text snippets with the goal of removing such text. Objective: We developed a natural language processing system (...) | Scopus | 2021 | 10.2196/23101 | Chang D., Lin E., Brandt C., Taylor R.A. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120171749&doi=10.2196%2f23101&partnerID=40&md5=75cd7707fc3f80c27955fa4d85f4c2a5 | United States | augmented language models, semantic similarity, knowledge graph embedding | validation research | technique | health |
| Journal Article | Integrating and Navigating Engineering Design Decision-Related Knowledge Using Decision Knowledge Graph | Decision support; Design; Knowledge graph; Navigation; Searching(...) | Designers are usually facing a problem of finding information from a huge amount of unstructured textual documents in order to prepare for a decision to be made. The major challenge is that knowledge embedded in the textual documents are difficult to search at a semantic level and therefore not ready to support decisions in a timely manner. To address this challenge, in this paper we propose a knowledge-graph-based method for integrating and navigating decision-related knowledge in engineering d(...) | ScienceDirect | 2021 | 10.1016/j.aei.2021.101366 | Hao J., Zhao L., Milisavljevic-Syed J., Ming Z. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111263006&doi=10.1016%2fj.aei.2021.101366&partnerID=40&md5=e05d292e5b9c4805f77d8c0e525b720a | China, United Kingdom | entity extraction, relation extraction, semantic search | solution proposal | method | engineering |
| Conference Paper | Intelligent Question Answering System Based on Entrepreneurial Incubation Knowledge Graph | Business incubation; Intelligent questions and answers; Knowledge graph; Natural language processing(...) | With the development of science and technology, the importance of innovation for the development of science and technology has become more and more important. In the current era of information explosion, in order to meet the needs of existing enterprises and individuals for obtaining entrepreneurial information, this system has designed an intelligent question-and-answer system based on the entrepreneurial incubation knowledge graph. The system locates the field of innovation, uses crawler softw(...) | IEEE | 2021 | 10.1109/prai53619.2021.9551028 | Feng S., Chen H., Huang M., Wu Y. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117950989&doi=10.1109%2fPRAI53619.2021.9551028&partnerID=40&md5=2e295cc636cd7ccebba79863c0e65b62 | China | question answering | solution proposal | tool | business |
| Conference Paper | Interactive Domain-Specific Knowledge Graphs from Text: a Covid-19 Implementation | COVID-19; Information retrieval software; Knowledge graphs; Natural language processing; Personalized analytics(...) | Information creation runs at a higher rate than information assimilation, creating an information gap for domain specialists that usual information frameworks such as search engines are unable to bridge. Knowledge graphs have been used to summarize large amounts of textual data, therefore facilitating information retrieval, but they require programming and machine learning skills not usually available to domains specialists. To bridge this gap, this work proposes a framework, KG4All (Knowledge G(...) | Scopus | 2021 | 10.1007/978-3-030-77417-2_18 | de Sousa V.M., Kern V.M. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111379685&doi=10.1007%2f978-3-030-77417-2_18&partnerID=40&md5=2d50742b21c49abc22919efb7e1e28a3 | Brazil | entity extraction, relation extraction, semantic search | solution proposal | tool | health |
| Conference Paper | Joint Biomedical Entity and Relation Extraction with Knowledge-Enhanced Collective Inference | Binding energy; Computational linguistics; Biomedical text; Collective inference; Domain information extraction; Domain knowledge; Entity extractions; External knowledge; Knowledge graphs; News domain; Relation extraction; State of the art; Knowledge graph(...) | Compared to the general news domain, information extraction (IE) from biomedical text requires much broader domain knowledge. However, many previous IE methods do not utilize any external knowledge during inference. Due to the exponential growth of biomedical publications, models that do not go beyond their fixed set of parameters will likely fall behind. Inspired by how humans look up relevant information to comprehend a scientific text, we present a novel framework that utilizes external knowl(...) | ACL | 2021 | - | Lai T., Ji H., Zhai C., Tran Q.H. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114519900&partnerID=40&md5=a37a60f89adeb6120ac8af03ee7f338e | United States | entity extraction, relation extraction | validation research | technique | health |
| Conference Paper | Joint Entity and Relation Extraction Method Based on Knowledge Representation Attention | relation extraction;knowledge representation;joint extraction;knowledge graph(...) | Relation extraction is a fundamental task in natural language processing and is a key step in information extraction tasks and construction of large-scale knowledge graphs, etc. Knowledge graph ontology information is useful for guiding triplet construction, but existing methods do not make full use of relevant information such as relation. Therefore, this paper proposes a joint extraction method of subject-aware entity relation combined with knowledge relation representation. The relation infor(...) | IEEE | 2021 | 10.1109/iscipt53667.2021.00160 | D. Gu; Y. Wang; B. Song | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9644527 | China | entity extraction, relation extraction | validation research | technique | - |
| Conference Paper | Joint Learning of Representations for Web-Tables, Entities and Types Using Graph Convolutional Network | Benchmarking; Computational linguistics; Convolution; Embeddings; Knowledge representation; Syntactics; Benchmark datasets; Convolutional networks; GraphicaL model; Joint learning; Knowledge graphs; Multiple state; Syntactic structure; Web tables; Convolutional neural networks(...) | Existing approaches for table annotation with entities and types either capture the structure of table using graphical models, or learn embeddings of table entries without accounting for the complete syntactic structure. We propose TabGCN, which uses Graph Convolutional Networks to capture the complete structure of tables, knowledge graph and the training annotations, and jointly learns embeddings for table elements as well as the entities and types. To account for knowledge incompleteness, TabG(...) | ACL | 2021 | - | Pramanick A., Bhattacharya I. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107282503&partnerID=40&md5=6fb6b4f0ff73a69c338af18f7347d97e | - | entity classification | validation research | technique | - |
| Journal Article | Kepler: a Unified Model for Knowledge Embedding and Pre-Trained Language Representation | - | Abstract Pre-trained language representation models (PLMs) cannot well capture factual knowledge from text. In contrast, knowledge embedding (KE) methods can effectively represent the relational facts in knowledge graphs (KGs) with informative entity embeddings, but conventional KE models cannot take full advantage of the abundant textual information. In this paper, we propose a unified model for Knowledge Embedding and Pre-trained LanguagERepresentation (KEPLER), which can not only better integ(...) | ACL | 2021 | 10.1162/tacl_a_00360 | Wang, Xiaozhi and Gao, Tianyu and Zhu, Zhaocheng and Zhang, Zhengyan and Liu, Zhiyuan and Li, Juanzi and Tang, Jian | https://aclanthology.org/2021.tacl-1.11 | Canada, China, United States | knowledge graph embedding, augmented language models | validation research | technique; resource | - |
| Conference Paper | Km-Bart: Knowledge Enhanced Multimodal Bart for Visual Commonsense Generation | Computational linguistics; Image enhancement; Knowledge based systems; Knowledge management; Commonsense knowledge; Knowledge based; Knowledge graphs; Language model; Modeling performance; Multi-modal; Multimodal inputs; Multimodal models; Pre-training; Sequence models; Knowledge graph(...) | We present Knowledge Enhanced Multimodal BART (KM-BART), which is a Transformer-based sequence-to-sequence model capable of reasoning about commonsense knowledge from multimodal inputs of images and texts. We adapt the generative BART architecture (Lewis et al., 2020) to a multimodal model with visual and textual inputs. We further develop novel pretraining tasks to improve the model performance on the Visual Commonsense Generation (VCG) task. In particular, our pretraining task of Knowledge-bas(...) | ACL | 2021 | - | Xing Y., Shi Z., Meng Z., Lakemeyer G., Ma Y., Wattenhofer R. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118931504&partnerID=40&md5=6ac5818aafafe229bb12cfbe8bfddc02 | Switzerland, Germany | augmented language models | validation research | technique | - |
| Conference Paper | Knowgraph@Iitk at Semeval-2021 Task 11: Building Knowledge Graph for Nlp Research | - | Research in Natural Language Processing is making rapid advances, resulting in the publication of a large number of research papers. Finding relevant research papers and their contribution to the domain is a challenging problem. In this paper, we address this challenge via the SemEval 2021 Task 11: NLPContributionGraph, by developing a system for a research paper contributions-focused knowledge graph over Natural Language Processing literature. The task is divided into three sub-tasks: extractin(...) | ACL | 2021 | 10.18653/v1/2021.semeval-1.57 | Shailabh, Shashank and Chaurasia, Sajal and Modi, Ashutosh | https://aclanthology.org/2021.semeval-1.57 | India | entity extraction, relation extraction, triple classification | validation research | method | scholarly domain |
| Conference Paper | Knowledge Augmented Language Models for Causal Question Answering | Causal knowledge graphs; Causal question answering; Causal reasoning; Language models(...) | The task of causal question answering broadly involves reasoning about causal relations and causality over a provided premise. Causal question answering can be expressed across a variety of tasks including commonsense question answering, procedural reasoning, reading comprehension, and abductive reasoning. Transformer-based pretrained language models have shown great promise across many natural language processing (NLP) applications. However, these models are reliant on distributional knowledge (...) | Scopus | 2021 | - | Dalal D. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121137603&partnerID=40&md5=a2b76c001813760b3b290b87017964b3 | Ireland | augmented language models, question answering | validation research | technique | - |
| Journal Article | Knowledge Based Deep Inception Model for Web Page Classification | Web page classification; transfer learning; knowledge graph embedding; pre-trained model(...) | Web Page Classification is decisive for information retrieval and management task and plays an imperative role for natural language processing (NLP) problems in web engineering. Traditional machine learning algorithms excerpt covet features from web pages whereas deep leaning algorithms crave features as the network goes deeper. Pre-trained models such as BERT attains remarkable achievement for text classification and continue to show state-of-the-art results. Knowledge Graphs can provide rich s(...) | WoS | 2021 | 10.13052/jwe1540-9589.2075 | Gupta A,Bhatia R | http://dx.doi.org/10.13052/jwe1540-9589.2075 | India | text classification, knowledge graph embedding | validation research | technique | - |
| Conference Paper | Knowledge Graph Analysis of Russian Trolls | Entity extraction; Relationship analysis of troll tweets; Sentiment analysis; Triple extraction(...) | Social media, such as Twitter, have been exploited by trolls to manipulate political discourse and spread disinformation during the 2016 US Presidential Election. Trolls are users of social media accounts created with intentions to influence the public opinion by posting or reposting messages containing misleading or inflammatory information with malicious intentions. There has been previous research that focused on troll detection using Machine Learning approaches, and troll understanding using(...) | Scopus | 2021 | 10.5220/0010605403350342 | Li C.-Y., Chun S.A., Geller J. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111720759&doi=10.5220%2f0010605403350342&partnerID=40&md5=818594d70343c5edec45007d1aa3d1c9 | United States | semantic search, text analysis | solution proposal | method | social media |
| Conference Paper | Knowledge Graph Augmented Advanced Learning Models for Commonsense Reasoning | Artificial Intelligence; Commonsense QA; ConceptNet; Hierarchical attention mechanism; Knowledge graphs; LSTM; Machine learning; Natural language processing; Neural networks(...) | Machine learning is the key solution to many AI issues, but learning models rely heavily on specific training data. While a Bayesian setup can be used to incorporate some learning patterns with previous knowledge, those patterns can not access any organized world knowledge on requirements. The primary objective is to enable human-capable machines in ordinary everyday circumstances to estimate and make presumptions. In this paper we propose to respond to such common sense issues through a textual(...) | Scopus | 2021 | 10.1088/1757-899x/1022/1/012038 | Pothuri A., Veeramallu H.S.R., Malik P. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100733787&doi=10.1088%2f1757-899X%2f1022%2f1%2f012038&partnerID=40&md5=e45908062dccf8cdbc1c140ef091e6dd | India | augmented language models, question answering | validation research | technique | - |
| Conference Paper | Knowledge Graph Construction and Intelligent Question Answering on Science and Technology Intermediary Service | Information overload; Information retrieval; Intelligent question answering technology; Knowledge graph; Natural language processing(...) | With the rapid development of the Internet, while it facilitates users to obtain information, it also increases information overload. Although a lot of data have been divided into categories, it is still a big challenge to retrieve effective information from thousands of categories and their subcategories. For professional business, we need more efficient information organization and interactive interface to reduce the complexity of information retrieval. This paper designs an intelligent questi(...) | IEEE | 2021 | 10.1109/prai53619.2021.9551099 | Feng S., Tu Z., Huang M., Wu Y. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117951124&doi=10.1109%2fPRAI53619.2021.9551099&partnerID=40&md5=f075c34b5d44f2b900f575d32428fa4c | China | question answering | solution proposal | tool | scholarly domain |
| Conference Paper | Knowledge Graph Construction of High-Performance Computing Learning Platform | Clustering algorithms; Computational linguistics; Deep learning; Knowledge acquisition; Knowledge based systems; Knowledge representation; Learning systems; Natural language processing systems; Semantics; Clipping algorithms; Efficient learning; High performance computing; Intelligent educations; Statistical language models; Structured graphs; Unstructured texts; Unsupervised extraction; Engineering education(...) | With the development of intelligent education, it has become one of the more efficient learning schemes to construct the knowledge graph which can excavate the knowledge base. People generally use RDF triples and use languages such as OWL to construct knowledge graphs, but this method has problems such as limited expression ability and too much manual annotation. In this paper, we propose a framework that combines statistical language models, neural network language models, and clustering and cl(...) | Scopus | 2021 | 10.1088/1742-6596/1748/2/022035 | Dong T., Tang L., Peng J., Zhong S., Luo H. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102367527&doi=10.1088%2f1742-6596%2f1748%2f2%2f022035&partnerID=40&md5=a0a8d953fcdaf14a7eeb8d6d4d467aa6 | China | entity extraction, relation extraction | solution proposal | tool | education |
| Journal Article | Knowledge Graph Embedding Based on Multi-View Clustering Framework | Knowledge graph; knowledge representation; multi-view clustering; semantic analysis(...) | Knowledge representation is one of the critical problems in knowledge engineering and artificial intelligence, while knowledge embedding as a knowledge representation methodology indicates entities and relations in knowledge graph as low-dimensional, continuous vectors. In this way, knowledge graph is compatible with numerical machine learning models. Major knowledge embedding methods employ geometric translation to design score function, which is weak-semantic for natural language processing. T(...) | IEEE | 2021 | 10.1109/tkde.2019.2931548 | Xiao H., Chen Y., Shi X. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091483090&doi=10.1109%2fTKDE.2019.2931548&partnerID=40&md5=f2c9a44444e11f9e77252536c480beb6 | China | knowledge graph embedding | validation research | technique | - |
| Conference Paper | Knowledge Graph Mining for Realty Domain Using Dependency Parsing and Qat Models | dependency parsing; knowledge-graph; neural network; ontology; QAT; real estates(...) | The real estate business has a lot of risks, and in order to minimize them, you need a lot of information from different sources. Systems based on natural language processing can help customers find this information more easily: question answering, information retrieval, etc. The existing method of question answering requires data aligned with possible questions, which are not easy to obtain, in contrast, the knowledge-graph provides structured information. In this paper, we propose semi-automat(...) | ScienceDirect | 2021 | 10.1016/j.procs.2021.10.004 | Zamiralov A., Sohin T., Butakov N. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120552308&doi=10.1016%2fj.procs.2021.10.004&partnerID=40&md5=64d84c5f07096c24a5aa1b14e03bb744 | Russian Federation | ontology construction, semantic search, relation extraction, question answering | solution proposal | method | business |
| Conference Paper | Knowledge Graph of Mergers and Acquisitions | Knowledge Graphs; Natural Language Processing(...) | Context driven decision making is a key factor to make critical decisions in business applications. We present the design and application of a knowledge graph to aid the context driven decision making for studying the patterns in Mergers and Acquisitions (MA) activities in the industry. Using text data from news articles we make use of a Natural Language Processing pipeline to extract entities and relations to build a knowledge graph. The entity recognition model was 90.97% accurate in detecting(...) | IEEE | 2021 | 10.1109/esci50559.2021.9397038 | Bhoomkar Y., Vernekar S., Kulkarni A., Kulkarni P., Aniyan A. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104583816&doi=10.1109%2fESCI50559.2021.9397038&partnerID=40&md5=a46f6e917d4b231c8202a0d0af384078 | India | entity extraction, relation extraction | solution proposal | method | business |
| Journal Article | Knowledge Graphs for Covid-19: an Exploratory Review of the Current Landscape | COVID-19; knowledge graph; natural language processing; drug repurposing(...) | Background: Searching through the COVID-19 research literature to gain actionable clinical insight is a formidable task, even for experts. The usefulness of this corpus in terms of improving patient care is tied to the ability to see the big picture that emerges when the studies are seen in conjunction rather than in isolation. When the answer to a search query requires linking together multiple pieces of information across documents, simple keyword searches are insufficient. To answer such comp(...) | WoS | 2021 | 10.3390/jpm11040300 | Chatterjee A,Nardi C,Oberije C,Lambin P | http://dx.doi.org/10.3390/jpm11040300 | Italy, Netherlands | semantic search | secondary research | guidelines | health |
| Conference Paper | Knowledge-Enriched Event Causality Identification Via Latent Structure Induction Networks | - | Identifying causal relations of events is an important task in natural language processing area. However, the task is very challenging, because event causality is usually expressed in diverse forms that often lack explicit causal clues. Existing methods cannot handle well the problem, especially in the condition of lacking training data. Nonetheless, humans can make a correct judgement based on their background knowledge, including descriptive knowledge and relational knowledge. Inspired by it, (...) | ACL | 2021 | 10.18653/v1/2021.acl-long.376 | Cao, Pengfei and Zuo, Xinyu and Chen, Yubo and Liu, Kang and Zhao, Jun and Chen, Yuguang and Peng, Weihua | https://aclanthology.org/2021.acl-long.376 | China | augmented language models | validation research | method | - |
| Conference Paper | Learning Event Graph Knowledge for Abductive Reasoning | Computational linguistics; Learning systems; Abductive reasoning; Auto encoders; Commonsense knowledge; Event graphs; Language model; Latent variable; Question Answering; Reading comprehension; Reasoning framework; Reasoning tasks; Knowledge graph(...) | Abductive reasoning aims at inferring the most plausible explanation for observed events, which would play critical roles in various NLP applications, such as reading comprehension and question answering. To facilitate this task, a narrative text based abductive reasoning task aNLI is proposed, together with explorations about building reasoning framework using pretrained language models. However, abundant event commonsense knowledge is not well exploited for this task. To fill this gap, we prop(...) | ACL | 2021 | - | Du L., Ding X., Liu T., Qin B. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118921647&partnerID=40&md5=f752f2d33a418531b0826cedefd96ca0 | China | natural language inference, augmented language models | validation research | technique | - |
| Conference Paper | Learning Numeracy: a Simple yet Effective Number Embedding Approach Using Knowledge Graph | - | Numeracy plays a key role in natural language understanding. However, existing NLP approaches, not only traditional word2vec approach or contextualized transformer-based language models, fail to learn numeracy. As the result, the performance of these models is limited when they are applied to number-intensive applications in clinical and financial domains. In this work, we propose a simple number embedding approach based on knowledge graph. We construct a knowledge graph consisting of number ent(...) | ACL | 2021 | 10.18653/v1/2021.findings-emnlp.221 | Duan, Hanyu and Yang, Yi and Tam, Kar Yan | https://aclanthology.org/2021.findings-emnlp.221 | Hong Kong | knowledge graph embedding | validation research | technique | - |
| Journal Article | Leveraging Online Behaviors for Interpretable Knowledge-Aware Patent Recommendation | Interpretable knowledge-aware recommendation; Online behaviors; Patent recommendation(...) | Purpose: Patent trade recommendations necessitate recommendation interpretability in addition to recommendation accuracy because of patent transaction risks and the technological complexity of patents. This study designs an interpretable knowledge-aware patent recommendation model (IKPRM) for patent trading. IKPRM first creates a patent knowledge graph (PKG) for patent trade recommendations and then leverages paths in the PKG to achieve recommendation interpretability. Design/methodology/approac(...) | Scopus | 2021 | 10.1108/intr-08-2020-0473 | Du W., Yan Q., Zhang W., Ma J. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107717528&doi=10.1108%2fINTR-08-2020-0473&partnerID=40&md5=de0eec8cad311f3e51382373d25b8025 | China, Hong Kong | semantic search | validation research | method | law |
| Conference Paper | Lexicon-Based Graph Convolutional Network for Chinese Word Segmentation | - | Precise information of word boundary can alleviate the problem of lexical ambiguity to improve the performance of natural language processing (NLP) tasks. Thus, Chinese word segmentation (CWS) is a fundamental task in NLP. Due to the development of pre-trained language models (PLM), pre-trained knowledge can help neural methods solve the main problems of the CWS in significant measure. Existing methods have already achieved high performance on several benchmarks (e.g., Bakeoff-2005). However, re(...) | ACL | 2021 | 10.18653/v1/2021.findings-emnlp.248 | Huang, Kaiyu and Yu, Hao and Liu, Junpeng and Liu, Wei and Cao, Jingxiang and Huang, Degen | https://aclanthology.org/2021.findings-emnlp.248 | China | augmented language models | validation research | technique | - |
| Conference Paper | Lnn-El: a Neuro-Symbolic Approach to Short-Text Entity Linking | Computational linguistics; Computer circuits; Formal logic; Heuristic methods; Natural language processing systems; Black boxes; Conversational systems; First order logic; Interpretable rules; Knowledge graphs; Neural learning; Performance; Question answering systems; Rule based; Short texts; Knowledge graph(...) | Entity linking (EL), the task of disambiguating mentions in text by linking them to entities in a knowledge graph, is crucial for text understanding, question answering or conversational systems. Entity linking on short text (e.g., single sentence or question) poses particular challenges due to limited context. While prior approaches use either heuristics or black-box neural methods, here we propose LNN-EL, a neuro-symbolic approach that combines the advantages of using interpretable rules based(...) | ACL | 2021 | - | Jiang H., Gurajada S., Lu Q., Neelam S., Popa L., Sen P., Li Y., Gray A. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118921105&partnerID=40&md5=574c8cee5c137482019988dffa5bacea | United States | entity linking | validation research | technique | - |
| Conference Paper | Lome: Large Ontology Multilingual Extraction | Knowledge representation; Co-reference resolutions; Knowledge graphs; Multilingual trainings; Relation extraction; State of the art; Temporal relation; Text document; Third parties; Computational linguistics(...) | We present Lome, a system for performing multilingual information extraction. Given a text document as input, our core system identifies spans of textual entity and event mentions with a FrameNet (Baker et al., 1998) parser. It subsequently performs coreference resolution, fine-grained entity typing, and temporal relation prediction between events. By doing so, the system constructs an event and entity focused knowledge graph. We can further apply third-party modules for other types of annotatio(...) | ACL | 2021 | - | Xia P., Qin G., Vashishtha S., Chen Y., Chen T., May C., Harman C., Rawlins K., White A.S., van Durme B. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107282328&partnerID=40&md5=0d2242fdaff90b4f240f8baae701e87a | United States | entity extraction, entity classification, relation extraction, relation classification | validation research | tool | - |
| Conference Paper | Mg-Bert: Multi-Graph Augmented Bert for Masked Language Modeling | - | Pre-trained models like Bidirectional Encoder Representations from Transformers (BERT), have recently made a big leap forward in Natural Language Processing (NLP) tasks. However, there are still some shortcomings in the Masked Language Modeling (MLM) task performed by these models. In this paper, we first introduce a multi-graph including different types of relations between words. Then, we propose Multi-Graph augmented BERT (MG-BERT) model that is based on BERT. MG-BERT embeds tokens while taki(...) | ACL | 2021 | 10.18653/v1/2021.textgraphs-1.12 | BehnamGhader, Parishad and Zakerinia, Hossein and Soleymani Baghshah, Mahdieh | https://aclanthology.org/2021.textgraphs-1.12 | Iran | augmented language models | validation research | technique | - |
| Conference Paper | Multimodal Language Modelling on Knowledge Graphs for Deep Video Understanding | intent detection; knowledge graphs; language model; scene description; slot filling; speaker diarization; transformers(...) | The natural language processing community has had a major interest in auto-regressive [4, 13] and span-prediction based language models [7] recently, while knowledge graphs are often referenced for common-sense based reasoning and fact-checking models. In this paper, we present an equivalence representation of span-prediction based language models and knowledge-graphs to better leverage recent developments of language modelling for multi-modal problem statements. Our method performed well, espec(...) | Scopus | 2021 | 10.1145/3474085.3479220 | Anand V., Ramesh R., Jin B., Wang Z., Lei X., Lin C.-Y. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119362395&doi=10.1145%2f3474085.3479220&partnerID=40&md5=0f6c2c4ebc8eb5060ace30f8ebd1967a | United States | augmented language models, text analysis | validation research | technique; resource | - |
| Conference Paper | Natural Language Inference Using Evidence from Knowledge Graphs | Knowledge graphs; Natural Language Inference; Natural language processing; Neural networks(...) | Knowledge plays an essential role in inference, but is less explored by previous works in the Natural Language Inference (NLI) task. Although traditional neural models obtained impressive performance on standard benchmarks, they often encounter performance degradation when being applied to knowledge-intensive domains like medicine and science. To address this problem and further fill the knowledge gap, we present a simple Evidence-Based Inference Model (EBIM) to integrate clues collected from kn(...) | Scopus | 2021 | 10.1007/978-981-16-5943-0_1 | Jia B., Xu H., Guo M. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115693431&doi=10.1007%2f978-981-16-5943-0_1&partnerID=40&md5=39cef3af4016f1cedf2f9a2873da531c | China | natural language inference | validation research | technique | - |
| Conference Paper | Oekg: the Open Event Knowledge Graph | Natural language processing systems; Open Data; Global impacts; Knowledge graphs; Multiple applications; Named entity recognition; News articles; Olympic games; Question Answering; Temporal knowledge; Knowledge representation(...) | Accessing and understanding contemporary and historical events of global impact such as the US elections and the Olympic Games is a major prerequisite for cross-lingual event analytics that investigate event causes, perception and consequences across country borders. In this paper, we present the Open Event Knowledge Graph (OEKG), a multilingual, event-centric, temporal knowledge graph composed of seven different data sets from multiple application domains, including question answering, entity r(...) | Scopus | 2021 | - | Gottschalk S., Kacupaj E., Abdollahi S., Alves D., Amaral G., Koutsiana E., Kuculo T., Major D., Mello C., Cheema G.S., Sittar A., Swati, Tahmasebzadeh G., Thakkar G. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103195785&partnerID=40&md5=8be5bde6108e7924dc9d07a2b49d21cd | Germany, United Kingdom, Croatia, Slovenia | semantic search, question answering | solution proposal | resource | history |
| Journal Article | On the Impact of Knowledge-Based Linguistic Annotations in the Quality of Scientific Embeddings | Natural language processing, Linguistic analysis, Knowledge graphs, Embeddings(...) | In essence, embedding algorithms work by optimizing the distance between a word and its usual context in order to generate an embedding space that encodes the distributional representation of words. In addition to single words or word pieces, other features which result from the linguistic analysis of text, including lexical, grammatical and semantic information, can be used to improve the quality of embedding spaces. However, until now we did not have a precise understanding of the impact that (...) | ScienceDirect | 2021 | 10.1016/j.future.2021.02.019 | Andres Garcia-Silva and Ronald Denaux and Jose Manuel Gomez-Perez | https://www.sciencedirect.com/science/article/pii/S0167739X21000716 | Spain | augmented language models | validation research | technique | scholarly domain |
| Conference Paper | Parameter-Efficient Domain Knowledge Integration from Multiple Sources for Biomedical Pre-Trained Language Models | - | Domain-specific pre-trained language models (PLMs) have achieved great success over various downstream tasks in different domains. However, existing domain-specific PLMs mostly rely on self-supervised learning over large amounts of domain text, without explicitly integrating domain-specific knowledge, which can be essential in many domains. Moreover, in knowledge-sensitive areas such as the biomedical domain, knowledge is stored in multiple sources and formats, and existing biomedical PLMs eithe(...) | ACL | 2021 | 10.18653/v1/2021.findings-emnlp.325 | Lu, Qiuhao and Dou, Dejing and Nguyen, Thien Huu | https://aclanthology.org/2021.findings-emnlp.325 | China, United States | augmented language models | validation research | technique | health |
| Conference Paper | Patentminer: Patent Vacancy Mining Via Context-Enhanced and Knowledge-Guided Graph Attention | Co-occurrence relationship; Graph attention networks; Knowledge graph; Link prediction(...) | Although there are a small number of work to conduct patent research by building knowledge graph, but without constructing patent knowledge graph using patent documents and combining latest natural language processing methods to mine hidden rich semantic relationships in existing patents and predict new possible patents. In this paper, we propose a new patent vacancy prediction approach named PatentMiner to mine rich semantic knowledge and predict new potential patents based on knowledge graph ((...) | Scopus | 2021 | 10.1007/978-981-16-6471-7_17 | Wu G., Xu B., Qin Y., Kong F., Liu B., Zhao H., Chang D. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119408902&doi=10.1007%2f978-981-16-6471-7_17&partnerID=40&md5=fb6bef277f8f9069b3f3016dce0fce59 | China | entity extraction, relation extraction, link prediction | solution proposal | method | law |
| Conference Paper | Qna System on Educational Textbooks: Digital Library Doubt Support System | Entity Extraction; Knowledge Graph; Natural Language Processing; Parsing; Question(...) | The domain of content and knowledge on any particular topic of someone's interest is ever - growing. This broadening of the knowledge base of a subject combined with the easy accessibility to the Internet through various devices has resulted in easy access to the loads of content when a search related to the topic is made. But this gigantic collection of data also possesses a challenge. One needs to make some efforts to find the desired information. For this purpose, an individual might be requi(...) | IEEE | 2021 | 10.1109/incet51464.2021.9456357 | Gupta R., Dabas G., Yadav H., Hasnain N. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113312722&doi=10.1109%2fINCET51464.2021.9456357&partnerID=40&md5=f4a723bc2684c2136075a1c36932e79f | India | question answering | solution proposal | tool | education |
| Conference Paper | Question Answering System Based on Tourism Knowledge Graph | Big data; Graph Databases; Graphic methods; Information services; Knowledge representation; Natural language processing systems; Query processing; Tourism; User experience; Knowledge graphs; Named entity recognition; Natural language questions; Query statements; Question answering systems; Reasoning models; System performance evaluation; User satisfaction; Leisure industry(...) | Nowadays tourism information services only provide users with massive and fragmented information returned by independent network search which makes users often need to spend a lot of time and energy to find what they really want from the massive data. As a result, route designing is very complicated. In view of this situation, this study builds a tourism knowledge graph based on neo4j and constructs a question answering system (QA). Also, we carry out the model and system performance evaluation,(...) | Scopus | 2021 | 10.1088/1742-6596/1883/1/012064 | Sui Y. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105480835&doi=10.1088%2f1742-6596%2f1883%2f1%2f012064&partnerID=40&md5=6424ea21c50ba1b4f92de5f8b4a59173 | China | question answering | solution proposal | method | tourism |
| Conference Paper | Relation-Aware Bidirectional Path Reasoning for Commonsense Question Answering | - | Commonsense Question Answering is an important natural language processing (NLP) task that aims to predict the correct answer to a question through commonsense reasoning. Previous studies utilize pre-trained models on large-scale corpora such as BERT, or perform reasoning on knowledge graphs. However, these methods do not explicitly model the \textit{relations} that connect entities, which are informational and can be used to enhance reasoning. To address this issue, we propose a relation-aware (...) | ACL | 2021 | 10.18653/v1/2021.conll-1.35 | Wang, Junxing and Li, Xinyi and Tan, Zhen and Zhao, Xiang and Xiao, Weidong | https://aclanthology.org/2021.conll-1.35 | China | question answering | validation research | method | - |
| Journal Article | Relation-Based Multi-Type Aware Knowledge Graph Embedding | Graph attention network; Knowledge graph embedding; Multi-type; Ontology; Taxonomy tree(...) | Knowledge graph (KG) embedding projects the graph into a low-dimensional space and preserves the graph information. An essential part of a KG is the ontology, which always is organized as a taxonomy tree, depicting the type (or multiple types) of each entity and the hierarchical relationships among these types. The importance of considering the ontology during KG embedding lies in its ability to provide side-information, improving the downstream applications’ accuracy (e.g., link prediction, ent(...) | ScienceDirect | 2021 | 10.1016/j.neucom.2021.05.021 | Xue Y., Jin J., Song A., Zhang Y., Liu Y., Wang K. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108708387&doi=10.1016%2fj.neucom.2021.05.021&partnerID=40&md5=aafc431ce41a560e0bbf2ce1b33073ad | China | knowledge graph embedding, link prediction | validation research | technique | - |
| Conference Paper | Representation Learning of Remote Sensing Knowledge Graph for Zero-Shot Remote Sensing Image Scene Classification | Deep alignment network (DAN);remote sensing knowledge graph (RSKG);remote sensing image scene classification;zero-shot learning (ZSL)(...) | Although deep learning has revolutionized remote sensing image scene classification, current deep learning-based approaches highly depend on the massive supervision of the predetermined scene categories and have disappointingly poor performance on new categories which go beyond the predetermined scene categories. In reality, the classification task often has to be extended along with the emergence of new applications that inevitably involve new categories of remote sensing image scenes, so how t(...) | IEEE | 2021 | 10.1109/igarss47720.2021.9553667 | Y. Li; D. Kong; Y. Zhang; R. Chen; J. Chen | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9553667 | China | augmented language models | validation research | method | - |
| Conference Paper | Research on Application of Chinese Natural Language Processing in Constructing Knowledge Graph of Chronic Diseases | Knowledge Graph; Named entity recognition; Natural language processing; Relationship extraction(...) | Knowledge Graph can describe the concepts in the objective world and the relationships between these concepts in a structured way, and identify, discover and infer the relationships between things and concepts. It has been developed in the field of medical and health care. In this paper, the method of natural language processing has been used to build chronic disease knowledge graph, such as named entity recognition, relationship extraction. This method is beneficial to forecast analysis of chro(...) | IEEE | 2021 | 10.1109/cisce52179.2021.9445976 | Qin S., Xu C., Zhang F., Jiang T., Ge W., Li J. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111428835&doi=10.1109%2fCISCE52179.2021.9445976&partnerID=40&md5=19c9fd0e21a582d4faa8adebcee84e3f | China | entity extraction, relation extraction | solution proposal | method | health |
| Journal Article | Research on Automatic Question Answering of Generative Knowledge Graph Based on Pointer Network | Answer generative model; Entity recognition; Knowledge base question answering; Pointer generator network; Pre-trained language model(...) | Question-answering systems based on knowledge graphs are extremely challenging tasks in the field of natural language processing. Most of the existing Chinese Knowledge Base Question Answering(KBQA) can only return the knowledge stored in the knowledge base by extractive methods. Nevertheless, this processing does not conform to the reading habits and cannot solve the Outof- vocabulary(OOV) problem. In this paper, a new generative question answering method based on knowledge graph is proposed, i(...) | Scopus | 2021 | 10.3390/info12030136 | Liu S., Tan N., Ge Y., Lukač N. | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103184572&doi=10.3390%2finfo12030136&partnerID=40&md5=b7d6f1efbcf602de7ad7684072584228 | China, Slovenia | question answering, augmented language models | validation research | method | - |
| Conference Paper | Research on Improved Intelligent Generative Dialogue Algorithm Based on Knowledge Graph |