Adopting an Object-Oriented Data Model in Inductive Logic Programming

May 1999

Proceedings of the Twelfth International Florida Artificial Intelligence Research Society Conference (FLAIRS 1999)

Volume

Proceedings of the Twelfth International Florida Artificial Intelligence Research Society Conference (FLAIRS 1999)

All Papers

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 techniques for structuring knowledge representation in machine learning seems particularly appealing. Inductive Logic Programming (ILP) is a promising approach for the automated discovery of rules in knowledge based systems. We propose an object-oriented extension of ILP employing multi-theory logic programs as the representation language. We define a new learning problem and propose the corresponding learning algorithm. Our approach enables ILP to benefit of object-oriented domain modelling in the learning process, such as allowing structured domains to be directly mapped onto program constructs, or easing the management of large knowledge bases.