An Automated Knowledge Acquisition Tool Based on the Inferential Modeling Technique

Knowledge acquisition is the process that extracts the required knowledge from available sources, such as experts, textbooks and databases, for incorporation into a knowledge-based system. Knowledge acquisition is described as the first step in building expert systems and a major bottleneck in the efficient development and application of effective knowledge based expert systems. One cause of the problem is that the process of human reasoning we need to understand for knowledge-based system development is not available for direct observation. Moreover, the expertise of interest is typically not reportable due to the compilation of knowledge which results from extensive practice in a domain of problem solving activity. This is also a problem of modeling knowledge, which has been described as not a problem of accessing and translating what is known, but the familiar scientific and engineering problem of formalizing models for the first time. And this formalization process is especially difficult for knowledge engineers who are often faced with the difficult task of creating a knowledge model of a domain unfamiliar to them. In this paper, we propose an automated knowledge acquisition tool which is based on an implementation of the Inferential Modeling Technique. The Inferential Modeling Technique is derived from the Inferential Model which is a domain-independent categorization of knowledge types and inferences ［Chan 1992］. The model can serve as a template of the types of knowledge in a knowledge model of any domain.