ProjR: Embedding Structure Diversity for Knowledge Graph Completion

Abstract

Knowledge graph completion aims to find new true links between entities. In this paper, we consider an approach to embed a knowledge graph into a continuous vector space. Embedding methods, such as TransE, TransR and ProjE, are proposed in recent years and have achieved promising predictive performance. We discuss that a lot of substructures related with different relation properties in knowledge graph should be considered during embedding. We list 8 kinds of substructures and find that none of the existing embedding methods could encode all the substructures at the same time. Considering the structure diversity, we propose that a knowledge graph embedding method should have diverse representations for entities in different relation contexts and different entity positions. And we propose a new embedding method ProjR which combines TransR and ProjE together to achieve diverse representations by defining a unique combination operator for each relation. In ProjR, the input head entity-relation pairs with different relations will go through a different combination process. We conduct experiments with link prediction task on benchmark datasets for knowledge graph completion and the experiment results show that, with diverse representations, ProjR performs better compared with TransR and ProjE. We also analyze the performance of ProjR in the 8 different substructures listed in this paper and the results show that ProjR achieves better performance in most of the substructures.