Adopting an Object-Oriented Data Model in Inductive Logic Programming
Michaela Milano, Andrea Omicini, Fabrizio Riguzzi · The Florida AI Research Society · 1999
The increasing amount of infm’mation to be manage.d in knowledge-based systems has promoted, on one hand, the exploitation of machine lem’ning for the automated acquisition of knowledge mid. on the other hand. the adoption of object-oriented representation models for easing the maintenmlce. In this comext, adopting techniques for structuring hmwledge represematiou in machine learning seems particularly appealing. Inductive Logic Programming {ILP) is a promising approach tbr the automated discovery of rules it, kr, owledge based systems. We Dropose an object-orietd ed extension of ILP employing multi-theory logic prog,’ants as the representation language. We define st new Icarning problem ~md propose the corresponding learlting algorithm. Our approadl enables ILP ,o benefit o[ object-oriented domain modelling in ,he lemning process, such as allowing st.ructured clomains to be directly mapped onto progrmn constructs, or easing the management of large knowledge bases.