Meta-learning in Knowledge Base completion
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Updated
Aug 26, 2020 - Python
Meta-learning in Knowledge Base completion
Implementation of a learning and fragment-based rule inference engine -- M. Svatoš, S. Schockaert, J. Davis, and O. Kuželka: STRiKE: Rule-driven relational learning using stratified k-entailment, ECAI'20
🌮 Table-based KB Completer
Code for project on reasoning over multiple paths
Source code & appendices accompanying the AAAI2022 paper "Unifying Knowledge Base Completion with PU Learning to Mitigate the Observation Bias"
Knowledge Base Completion for Long-Tail Entities
Some papers on knowledge graph embedding
Python implementation of "Data-dependent Learning of Symmetric/Antisymmetric Relations for Knowledge Base Completion [Manabe+. 2018]"
QuatRE: Relation-Aware Quaternions for Knowledge Graph Embeddings (TheWebConf WWW 2022) (Pytorch)
HyperKG: Hyperbolic Knowledge Graph Embeddings for Knowledge Base Completion
Paper list for knowledge hypergraph
Graph Neural Networks for Knowledge Graph Link Prediction (WSDM 2022) (Pytorch)
STransE: a novel embedding model of entities and relationships in knowledge bases (NAACL 2016)
A Capsule Network-based Embedding Model for Knowledge Graph Completion and Search Personalization (NAACL 2019)
SimplE Embedding for Link Prediction in Knowledge Graphs
A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network (NAACL 2018) (Pytorch and Tensorflow)
🤖 A Python library for learning and evaluating knowledge graph embeddings
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