  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.
