Holism and Incremental Knowledge Acquisition

Abstract

Human experts tend to introduce intermediate terms in giving their explanations. The expert’s explanation of such terms is operational for the context that triggered the explanation, however term definitions remain often incomplete. Further, the expert’s (re) use of these terms is hierarchical (similar to natural language). In this paper, we argue that a hierarchical incremental knowledge acquisition process that captures the expert terms and operationalises these terms while incompletely defined makes the KA task more effective. Towards this we present our knowledge representation formalism Nested Ripple Down Rules (NRDR) that is a substantial extension to the Ripple Down Rule (RDR) KA framework. It allows simultaneous incremental modelling and knowledge acquisition. In this paper we analyse the conditions under which RDR converges towards the target knowledge base (KB). We will also show that the extra maintenance cost of an NRDR KB is minimal, and that the maintenance of NRDR requires similar effort to maintaining RDR for most of the KB development cycle.