QuatRE: Relation-Aware Quaternions for Knowledge Graph Embeddings

• URL: http://arxiv.org/abs/2009.12517v2
• Date: Tue, 8 Mar 2022 12:05:02 GMT
• Title: QuatRE: Relation-Aware Quaternions for Knowledge Graph Embeddings
• Authors: Dai Quoc Nguyen and Thanh Vu and Tu Dinh Nguyen and Dinh Phung
• Abstract summary: We propose a simple embedding model to learn quaternion embeddings for entities and relations in knowledge graphs. Our model aims to enhance correlations between head and tail entities given a relation within the Quaternion space with Hamilton product.
• Score: 19.386604643035145
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a simple yet effective embedding model to learn quaternion embeddings for entities and relations in knowledge graphs. Our model aims to enhance correlations between head and tail entities given a relation within the Quaternion space with Hamilton product. The model achieves this goal by further associating each relation with two relation-aware rotations, which are used to rotate quaternion embeddings of the head and tail entities, respectively. Experimental results show that our proposed model produces state-of-the-art performances on well-known benchmark datasets for knowledge graph completion. Our code is available at: \url{https://github.com/daiquocnguyen/QuatRE}.

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