QuatRE: Relation-Aware Quaternions for Knowledge Graph Embeddings

Dai Quoc Nguyen, Thanh Vu, Tu Dinh Nguyen, Dinh Phung

Keywords: Knowledge graph embeddings, Quaternion, Hamilton product

Abstract: We propose an effective embedding model, named QuatRE, to learn quaternion embeddings for entities and relations in knowledge graphs. QuatRE aims to enhance correlations between head and tail entities given a relation within the Quaternion space with Hamilton product. QuatRE achieves this goal by further associating each relation with two relation-aware quaternion vectors which are used to rotate the head and tail entities' quaternion embeddings, respectively. To obtain the triple score, QuatRE rotates the rotated embedding of the head entity using the normalized quaternion embedding of the relation, followed by a quaternion-inner product with the rotated embedding of the tail entity. Experimental results demonstrate that our QuatRE produces state-of-the-art performances on well-known benchmark datasets for knowledge graph completion.

One-sentence Summary: QuatRE further uses two relation-aware quaternion vectors for each relation to strengthen the correlations between the head and tail entities.

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Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:2009.12517/code)

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