Simultaneous modelling and knowledge acquisition using NRDR

Abstract

Incremental refinement methods of knowledge bases ease maintenance but fail to uncover the underlying domain model used by the expert. In this paper, we propose a new knowledge representation formalism for incremental acquisition and refinement of knowledge. It guides the expert in expressing his model of the domain during the actual knowledge acquisition process. This knowledge representation scheme, Nested Ripple Down Rules, is a substantial extension to Ripple Down Rule (RDR) knowledge acquisition framework. This paper introduces a theoretical framework for analysing the structure of RDR in general and NRDR in particular. Using this framework we analyse the conditions under which RDR converges towards the target knowledge base. Further, we analyse the conditions under which NRDR offers an effective approach for domain modelling. We discuss the maintenance problems of NRDR as a function of this convergence. We show that the maintenance of NRDR requires similar effort to maintaining RDR for most of the knowledge base development cycle. We show that when an NRDR knowledge base shows an increase in maintenance requirement in comparison with RDR during its development, this added requirement can be automatically handled.