Discovering Fine-Grained Semantics in Knowledge Graph Relations
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Abstract (arXiv)
When it comes to comprehending and analyzing multi-relational data, the semantics of relations are crucial. Polysemous relations between different types of entities, that represent multiple semantics, are common in real-world relational datasets represented by knowledge graphs. For numerous use cases, such as entity type classification, question answering and knowledge graph completion, the correct semantic interpretation of these relations is necessary. In this work, we provide a strategy for discovering the different semantics associated with abstract relations and deriving many sub-relations with fine-grained meaning. To do this, we leverage the types of the entities associated with the relations and cluster the vector representations of entities and relations. The suggested method is able to automatically discover the best number of sub-relations for a polysemous relation and determine their semantic interpretation, according to our empirical evaluation.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 12 of its 45 references that are in the map. First posted on arXiv 17 Feb 2022.
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12 of 45 references.
References · 12
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- 2014Embedding Entities and Relations for Learning and Inference in Knowledge Basescited by 929
- 2015A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classificationcited by 92
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- 2015Efficient Non-parametric Estimation of Multiple Embeddings per Word in Vector Spacecited by 35
- 2015TransG : A Generative Mixture Model for Knowledge Graph Embeddingcited by 28
- 2019CESI: Canonicalizing Open Knowledge Bases using Embeddings and Side Informationcited by 28
- 2015Inferring Missing Entity Type Instances for Knowledge Base Completion: New Dataset and Methodscited by 5
- 2018Knowledge Graph Embedding with Multiple Relation Projectionscited by 5
- 2018Neural Latent Relational Analysis to Capture Lexical Semantic Relations in a Vector Spacecited by 3
- 2020Entity Type Prediction in Knowledge Graphs using Embeddingscited by 3
- 2017Vector Embedding of Wikipedia Concepts and Entitiescited by 1
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