Learning Description Logic Ontologies. Five Approaches. Where Do They Stand?
This work provides a comparative overview for researchers in ontology learning, but it is incremental as it synthesizes existing methods without introducing new techniques.
The paper reviews five classical machine learning and data mining approaches for semi-automating the creation of description logic ontologies, including association rule mining, formal concept analysis, inductive logic programming, computational learning theory, and neural networks, and discusses their benefits and limitations.
The quest for acquiring a formal representation of the knowledge of a domain of interest has attracted researchers with various backgrounds into a diverse field called ontology learning. We highlight classical machine learning and data mining approaches that have been proposed for (semi-)automating the creation of description logic (DL) ontologies. These are based on association rule mining, formal concept analysis, inductive logic programming, computational learning theory, and neural networks. We provide an overview of each approach and how it has been adapted for dealing with DL ontologies. Finally, we discuss the benefits and limitations of each of them for learning DL ontologies.