NagE: Non-Abelian Group Embedding for Knowledge Graphs

• URL: http://arxiv.org/abs/2005.10956v3
• Date: Thu, 3 Sep 2020 14:44:44 GMT
• Title: NagE: Non-Abelian Group Embedding for Knowledge Graphs
• Authors: Tong Yang, Long Sha, Pengyu Hong
• Abstract summary: We show that a group-based embedding framework is essential for designing embedding models. Motivated by theoretical analysis, we have proposed a group theory-based knowledge graph embedding framework. We provide a generic recipe to construct embedding models associated with two instantiating examples.
• Score: 14.545770519120898
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We demonstrated the existence of a group algebraic structure hidden in relational knowledge embedding problems, which suggests that a group-based embedding framework is essential for designing embedding models. Our theoretical analysis explores merely the intrinsic property of the embedding problem itself hence is model-independent. Motivated by the theoretical analysis, we have proposed a group theory-based knowledge graph embedding framework, in which relations are embedded as group elements, and entities are represented by vectors in group action spaces. We provide a generic recipe to construct embedding models associated with two instantiating examples: SO3E and SU2E, both of which apply a continuous non-Abelian group as the relation embedding. Empirical experiments using these two exampling models have shown state-of-the-art results on benchmark datasets.

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