Domain-driven knowledge modelling for knowledge acquisition

Abstract

Abstract Knowledge modelling is undoubtedly a major problem in knowledge acquisition. Drawing from industrial case studies that have been carried out, the paper lists some key problems which still dog knowledge modelling. Next, it critically reviews current knowledge modelling techniques and tools and concludes that these real knowledge acquisition issues are not tackled by them. We consider the spelling out of these problems and the fact that they are not addressed by current tools and techniques to be a major contribution of this paper. The paper strongly argues for knowledge modelling to be domain-driven, i.e. driven by the nature of the domain being modelled. The key argument in this paper is that ignoring the nature or characterization of the domain inevitably results in knowledge imposition rather than knowledge acquisition as domains get shoe-horned into some (current) set of models, representations and tools. After examining the nature of domains, the paper proceeds to outline an emerging hypothesis for knowledge modelling. It concludes with a specification of a tool suite for addressing the issues identified in this paper.