Hyperbolic Knowledge Graph Embedding with Logical Pattern Learning

Abstract

Recently, knowledge graph embedding approaches which aim to learn latent semantic representations for entities and relations in the knowledge graph, have become an active research topic. In this paper, we focus on hyperbolic knowledge graph embedding with logical pattern learning, based on the following observations: (i) the hyperbolic embedding methods have shown promising performance for knowledge graphs, which always exhibit hierarchical patterns; (ii) the Givens rotation has an excellent effect on modeling the logical pattern relations in the knowledge graph; (iii) ontology axioms are important extra information that can derive a variety of logical patterns (rules), and should be taken into consideration when learning embeddings. Futhermore, embedding learning and rule learning will complement each other and can be integrated into a unified framework. Therefore, we propose a novel hyperbolic knowledge graph embedding model named HyperLP, which can simultaneously learn embeddings and logical patterns. With extensive expriments on benchmark datasets, we find that our proposed HyperLP model achieves superior perfomence compared with a line of state-of-the-art baselines.