A Novel Asymmetric Embedding Model for Knowledge Graph Completion

Abstract

Modeling knowledge graph completion by encoding each entity and relation into a continuous tensor space becomes very hot. Meanwhile, many models including TransE, TransH, TransR, CTransR, TransD, TranSpare, TransDR, STransE, DT, FT and OrbitE are proposed for knowledge graph completion. However, all these previous works take less attention to the asymmetrical and the imbalance of many relations (some relations link a subject and many objects, and other relations link many subjects and many objects). Therefore, this paper proposes a novel asymmetrical embedding model(AEM) for knowledge graph completion. Because of the different properties of the head and tail entities in the triplets of the same relationship, every head entity vector and every tail entity vector are weighted by the corresponding head relation vector and the corresponding tail relation vector, respectively. And then new entity vector representations are obtained and the new entity vectors in the same triple are similar. Because the AEM weights each dimension of the entity vectors, it can accurately represent the latent attributes of entities and relationships. Moreover, the number of parameters of the AEM is so small that it is easier to train. Finally, compared with previous embedding models, the AEM obtains a better link prediction performance through two benchmark datasets FB15K and WN18.