EmEL⁺⁺: Embeddings for εL⁺⁺ description logic

Abstract

Knowledge graph (KG) embedding models have recently gained increased attention. However, most of the existing models for KG embeddings ignore the structure and characteristics of the underlying ontology. In this work, we present EmEL++ embeddings - an ontology-based embedding model for the εL++ description logic. EmEL++ maps the classes and the relations in an ontology to an n-dimensional vector space such that the relations between classes and relations in the ontology are preserved in the vector space. We evaluate the proposed embeddings on four different datasets and show that the proposed embeddings outperform the traditional knowledge graph embeddings on the subsumption reasoning task.