  Rules complement and extend ontologies on the Semantic Web. We refer to these
rules as onto-relational since they combine DL-based ontology languages and
Knowledge Representation formalisms supporting the relational data model within
the tradition of Logic Programming and Deductive Databases. Rule authoring is a
very demanding Knowledge Engineering task which can be automated though
partially by applying Machine Learning algorithms. In this chapter we show how
Inductive Logic Programming (ILP), born at the intersection of Machine Learning
and Logic Programming and considered as a major approach to Relational
Learning, can be adapted to Onto-Relational Learning. For the sake of
illustration, we provide details of a specific Onto-Relational Learning
solution to the problem of learning rule-based definitions of DL concepts and
roles with ILP.
