Affiliations: [a] Faculty of Information Science and Technology, Dalian Maritime University, Dalian, China
| [b] Research Center for Language Intelligence, Dalian University of Foreign Languages, Dalian, China
Correspondence:
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Corresponding author. Guanyu Li, Faculty of Information Science and Technology, Dalian Maritime University, Dalian, China. E-mail: rabitlee@163.com.
Abstract: Capturing the composite embedding representation of a multi-hop relation path is an extremely vital task in knowledge graph completion. Recently, rotation-based relation embedding models have been widely studied to embed composite relations into complex vector space. However, these models make some over-simplified assumptions on the composite relations, resulting the relations to be commutative. To tackle this problem, this paper proposes a novel knowledge graph embedding model, named QuatGE, which can provide sufficient modeling capabilities for complex composite relations. In particular, our method models each relation as a rotation operator in quaternion group-based space. The advantages of our model are twofold: (1) Since the quaternion group is a non-commutative group (i.e., non-Abelian group), the corresponding rotation matrices of composite relations can be non-commutative; (2) The model has a more expressive setting with stronger modeling capabilities, which is flexible to model and infer the complete relation patterns, including: symmetry/anti-symmetry, inversion and commutative/non-commutative composition. Experimental results on four benchmark datasets show that the proposed method outperforms the existing state-of-the-art models for link prediction, especially on composite relations.
Keywords: Knowledge graph embedding, quaternion group, link prediction
