Learning Embeddings with Logical Structure and Lexical Information ofOntologies for Concept Subsumption

Abstract

Although various knowledge graph embedding models have been proposed, they cannot enhance prediction of subsumption and membership relations from ontologies. Therefore, more attention has been paid to ontology embedding methods that uses lexical information or logical structures of ontologies. However, most of the methods do not employ lexical information together with logical structures. In this paper, we propose a method capturing both lexical and logical characteristics from ontologies. In this method, we extend the complex embedding of RotatE by the hierarchical radial coordinate of HAKE and initialize entity embeddings by the vectors learned from lexical information. In the experiments, we show that the proposed method outperforms existing methods for the subsumption prediction tasks on the benchmark ontologies FoodOn, GO, and HeLiS.