Adopting an Object-Oriented Data Model in Inductive Logic Programming

Last modified by Andrea Omicini on 2020/10/12 16:59

Michela Milano, Andrea Omicini, Fabrizio Riguzzi

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.

12th International Florida AI Research Society Conference (FLAIRS'99), pages 273-279, 1-5 May 1999, AAAI Press, Orlando, FL, USA
AcmId = {707352},
Address = {Orlando, FL, USA},
Author = {Milano, Michela and Omicini, Andrea and Riguzzi, Fabrizio},
Booktitle = {12th International Florida AI Research Society Conference (FLAIRS'99)},
City = {Menlo Park, CA, USA},
Isbn = {0-1-57735-080-4},
Month = {1--5~} # may,
Pages = {273--279},
Publisher = {AAAI Press},
Title = {Adopting an Object-Oriented Data Model in Inductive Logic Programming},
Url = {},
Url-Pdf = {},
Year = 1999}



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