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Embedding Knowledge Graphs into Multi-vector Space

Knowledge representation learning, also known as knowledge embedding, projects entities and relations into continuous vector space to quantify entities and relations. Among various embedding models, the translation-based models are one branch which interpret relations as translations operating on the embeddings of entities. However, these models cannot well separate entities or relations with similar facts. In this paper, we introduce TransMult, a novel approach, which projects entities and relations into a multi-vector space instead of a single vector space. TransMult does not increase the number of parameters and computational complexity, but can well distinguish similar entities or relations. We explained the rationality of TransMult and we further explored the influence of the multiplicity of the vector space on the model. As far as we know, TransMult is the first multi-vector space approach for knowledge graph embedding. To this end, in this paper, we extend the existing models TransE and TransH with TransMult. Experimental studies validate the effectiveness of the proposed approach against baselines.