Knowledge graph completion by embedding with bi-directional projections

Abstract

Knowledge graph (KG) completion aims at predicting the unknown links between entities and relations. In this paper, we focus on this task through embedding a KG into a latent space. Existing embedding based approaches such as TransH usually perform the same operation on head and tail entities in a triple. Such way could ignore the different roles of head and tail entities in a relation. To resolve this problem, this paper proposes a novel method for KGs embedding by preforming bi-directional projections on head and tail entities. In this way, the different information of an entity could be elaborately captured when it plays different roles for a relation. The experimental results on multiple benchmark datasets demonstrate that our method significantly outperforms state-of-the-art methods.