An embedding-based approach to constructing OWL ontologies

Abstract

This paper presents a novel system OWLearner for automatically extracting axioms for OWL ontologies from RDF data using embedding models. In this system, ontology construction is transformed to the classification problem in machine learning and thus off-the-shelf tools can be employed to learn axioms in OWL. There are mainly three modules, namely, embedding, sampling, and training & learning. Large ontologies DBpedia and YAGO are used to validate the proposed approach. The experimental results show that OWLearner is able to learn high-quality expressive OWL axioms automatically and efficiently.