Adopting an Object-Oriented Data Model in Inductive Logic Programming

@talk{ooilpflairs1999,
    abstract = {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.},
    address = {Orlando, FL, USA},
    apice = {OoilpFlairs1999},
    author = {Milano, Michela and Omicini, Andrea and Fabrizio Riguzzi},
    date = {1999-05-05},
    howpublished = {12th International Florida AI Research Society Conference (FLAIRS'99)},
    language = {en},
    month = may,
    sort = {talk},
    speaker = {Milano, Michela},
    title = {Adopting an Object-Oriented Data Model in Inductive Logic Programming},
    type = {Talk},
    year = 1999
}