The semantics of relations are crucial for comprehending and analyzing multi-relational data. Polysemous relations between different types of entities (i.e. those 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 method 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 automatically discovers the best number of sub-relations for a polysemous relation and determine their semantic interpretation, according to our empirical evaluation.

Semantics-driven Refinement for Relational Data

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W10: SUKI: Workshop on Structured and Unstructured Knowledge Integration

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## Welcome to NAACL 2022!
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NAACL 2022

2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics

Nitisha Jain

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Semantics-driven Refinement for Relational Data

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