Contextualise Entities and Relations: An Interaction Method for Knowledge Graph Completion

Kai Chen, Ye Wang, Yitong Li, Aiping Li, Xiaojuan Zhao

Abstract: The incompleteness of Knowledge Graph (KG) stimulates substantial research on knowledge graph completion, however, current state-of-the-art embedding based methods represent entities and relations in a semantic-separated manner, overlooking the interacted semantics between them. In this paper, we introduce a novel entity-relation interaction mechanism, which learns contextualised entity and relation representations with each other. We feature entity interaction embeddings by adopting a translation distance based method which projects entities into a relation-interacted semantic space, and we augment relation embeddings using a bi-linear projection. Built upon our interaction mechanism, we experiment our idea using two decoders, namely a simple Feed-forward based Interaction Model (FIM) and a Convolutional network based Interaction Model (CIM). Through extensive experiments conducted on three benchmark datasets, we demonstrate the advantages of our interaction mechanism, both of them achieving state-of-the-art performance consistently.