Knowledge Acquisition as a Process of Model

Refinement

Enrico Motta, Tim Rajan, and Marc Eisenstadt

Human Cognition Research Laboratory
The Open University
Milton Keynes, MK7 6AA, U.K.

Abstract: The strengths and weaknesses of our earlier
system, KEATS-1, have led us to embark upon the design and implementation of a new knowledge engineering
environment, KEATS-2, which provides a novel, integrated framework for performing both bottom-up and top-down knowledge acquisition. In this paper we discuss the nature of the knowledge acquisition activities and we introduce the support tools embedded in KEATS-2. We characterize
knowledge acquisition as the composition of knowledge
elicitation, data analysis and domain conceptualization and we emphasize that a knowledge engineering tool has to support these activities as well as bridging the gap between acquiring the data and implementing the final system.

Acknowledgement: This research is supported by a grant from British Telecommunications, plc. Steven Rose and Mike Stewart of the Open University's Brain Research Group
provided valuable domain expertise.

1. THE PROBLEM OF KNOWLEDGE ACQUISITION

The most popular principle in knowledge based systems states that the performance of an expert system critically depends on the amount of knowledge embedded in the system (Feigenbaum, 1977). Therefore the knowledge engineer usually spends a great deal of time eliciting knowledge from domain experts and even more trying to make sense of the data acquired. The combined activity of eliciting, interpreting and organizing the knowledge acquired from the expert is called 'knowledge acquisition', and is often described as a lengthy and painful process. In fact, problems can arise, due to a number of factors, including the