Adopting an object-oriented data model in Inductive Logic Programming

The increasing amount of information to be managed in knowledge-based systems has promoted, on one hand, the exploitation of machine learning for the automated acquisition of knowledge and, on the other hand, the adoption of object-oriented representation models for easing the maintenance. In this context, adopting tech- niques for structuring knowledge representation in ma- chine learning seems particularly appealing. Inductive Logic Programming (ILP) is a promising ap- proach for the automated discovery of rules in knowl- edge based systems. We propose an object-oriented ex- tension of ILP employing multi-theory logic programs as the representation language. We dene a new learn- ing problem and propose the corresponding learning algorithm. Our approach enables ILP to benet of object-oriented domain modelling in the learning pro- cess, such as allowing structured domains to be directly mapped onto program constructs, or easing the man- agement of large knowledge bases.