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The quest for acquiring a formal representation of the knowledge of a domainof interest has attracted researchers with various backgrounds into a diversefield called ontology learning. We highlight classical machine learning anddata mining approaches that have been proposed for (semi-)automating thecreation of description logic (DL) ontologies. These are based on associationrule mining, formal concept analysis, inductive logic programming,computational learning theory, and neural networks. We provide an overview ofeach approach and how it has been adapted for dealing with DL ontologies.Finally, we discuss the benefits and limitations of each of them for learningDL ontologies.

Learning Description Logic Ontologies. Five Approaches. Where Do They Stand?