OnNER: An Ontology for Semantic Representation of Named Entities in Scholarly Publications

Abstract
A significant portion of scientific knowledge resides within scholarly publications, both in print and digital formats. Recent advancements in natural language processing and information extraction techniques have enhanced the accessibility of this knowledge for further automated querying and processing. Structured and semantically-aware representations, such as ontologies, play a crucial role in simplifying and integrating access to this vast pool of knowledge. While several ontologies have been developed to capture the structure and discourse of scientific publications, there is a notable scarcity of ontologies for succinctly representing named entities that are present in scholarly documents.
This paper introduces the Ontology for Named Entity Representation (OnNER) to address this gap. OnNER is designed to represent named entities – the terms identified and labeled using named entity recognition (NER) methods – from scholarly publications. The ontology provides a structured semantic representation of the named entities, how they are labeled, and where they occur. We discuss the overall design of OnNER, its integration with other ontologies, and demonstrate how the ontology facilitates advanced querying of named entities’ presence and collocation within and across publications.

OnNER Paper: https://ebooks.iospress.nl/doi/10.3233/FAIA241291

OnNER Documentation: https://theskailab.github.io/OnNER/onner-full.html

Cite: Reza, U., Zhang, X., & Hahmann, T. (2024). OnNER: An Ontology for Semantic Representation of Named Entities in Scholarly Publications. In: Proceedings of the 14th International Conference on Formal Ontology in Information Systems (FOIS 2024), Twente, The Netherlands, July 2024. Volume 394 of Frontiers in Artificial Intelligence and Applications, IOS Press. https://doi.org/10.3233/faia241291

Repository Description

• v1 → The OnNER ontology (TBox) in Turtle format; for other formats (RDF/XML, N-Triples, JSON-LD), please refer to the OnNER documentation
• evaluation
  • CQs-and-SPARQLs.md → Competency questions and corresponding SPARQL queries
  • example
    • input → Input files (in XML format) for the ABox generator
    • output → Output files (in TTL format) generated by the ABox generator
  • source
    • ABox → Source code for the ABox generator
    • SciTex → Source code for SciTex, which converts PDFs to XMLs
• docs → OnNER documentation is deployed from this directory
  • images → Diagrams of OnNER

Note: The TBox (Terminological Box) defines the schema or structure—i.e., classes, properties, and their relationships. The ABox (Assertional Box) contains instance-level data—i.e., assertions about individuals based on the TBox definitions.