Knowledge modeling for ontology-based multiatrribute classification system

Developers of the early knowledge-based systems expected that their systems would be ideally suited for reuse (Hayes-Roth, Waterman, & Lenat, 1983). These systems consisted of two parts: a knowledge base containing rules about the problem domain and an inference engine that used the knowledge base to provide solutions. The hope was that this approach could be applied to a variety of different domain tasks. Unfortunately, the overhead cost of constructing knowledge bases for new domains was often too high: the knowledge-acquisition "bottleneck" problem arose because it is difficult to describe and encode domain knowledge in the low-level terms required by the inference engine. The purpose of this paper is ontology bifurcation into domain ontology (a set of concepts and relations independent of any classification method) and method (application) ontology (set of concepts and relations) devoted to decision tree classification method.