Incorporating Domain and Range of Relations for Knowledge Graph Completion

Abstract

Knowledge graphs store facts as triples, with each containing two entities and one relation. Information of entities and relations are important for knowledge graph related tasks like link prediction. Knowledge graph embedding methods embed entities and relations into a continuous vector space and accomplish link prediction via calculation with embeddings. However, some embedding methods only focus on information of triples and ignore individual information about relations. For example, relations inherently have domain and range which will contribute much towards learning, even though sometimes they are not explicitly given in knowledge graphs. In this paper, we propose a framework TransX$_C$ (X can be replaced with E, H, R or D) to help preserve individual information of relations, which can be applied to multiple traditional translation-based embedding methods (i.e. TransE, TransH, TransR and TransD). In TransX$_C$, we use two logistic regression classifiers to model domain and range of relations respectively, and then we train the embedding model and classifiers jointly in order to include information of triples as well as domain and range of relations. The performance of TransX$_C$ are evaluated on link prediction task. Experimental results show that our method outperforms the corresponding translation-based model, indicating the effectiveness of considering domain and range of relations into link prediction.