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arXiv · 2009.12517

QuatRE: Relation-Aware Quaternions for Knowledge Graph Embeddings

Dai Quoc Nguyen·Thanh Vu·Tu Dinh Nguyen·Dinh Phung
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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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Dai Quoc Nguyen, Thanh Vu, Tu Dinh Nguyen, Dinh Phung. 2020-09-26. QuatRE: Relation-Aware Quaternions for Knowledge Graph Embeddings. https://arxiv.org/abs/2009.12517

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