TransT: Type-Based Multiple Embedding Representations for Knowledge Graph Completion

Knowledge graph completion with representation learning predicts new entity-relation triples from the existing knowledge graphs by embedding entities and relations into a vector space. Most existing methods focus on the structured information of triples and maximize the likelihood of them. However, they neglect semantic information contained in most knowledge graphs and the prior knowledge indicated by the semantic information. To overcome this drawback, we propose an approach that integrates the structured information and entity types which describe the categories of entities. Our approach constructs relation types from entity types and utilizes type-based semantic similarity of the related entities and relations to capture prior distributions of entities and relations. With the type-based prior distributions, our approach generates multiple embedding representations of each entity in different contexts and estimates the posterior probability of entity and relation prediction. Extensive experiments show that our approach outperforms previous semantics-based methods. The source code of this paper can be obtained from https://github.com/shh/transt.