The use of background knowledge is one of the distinguishing features of ILP
with respect to other approaches to machine learning. Yet the representation
formalisms traditionally chosen for the background knowledge in ILP seem to
ignore the latest developments in Knowledge Engineering such as standard
languages for ontologies.
In this paper we present a case study that shows how current ILP systems can
be made compliant with these standards in order to fulfill the expressive
requirements of emerging application areas like the Semantic Web.