Consistency-driven knowledge elicitation: using a learning-oriented knowledge representation that supports knowledge elicitation in NeoDISCIPLE

Abstract

A general approach to knowledge elicitation in interactive learning systems is presented which both improves a knowledge base by removing inconsistencies and extends the representation space for learning. This approach addresses the problem of learning "new terms" with interactive learning systems. Two methods that illustrate this approach are implemented in the learning apprentice system NeoDISCIPLE, using a concept-based representation that is very appropriate for learning. At the same time, the representation facilitates knowledge elicitation associated with human-oriented representations like, for instance, repertory grids. Both methods are consistency-driven in that they elicit knowledge from a human expert in order to remove inconsistencies in the knowledge pieces learned by NeoDISCIPLE. The input to these methods is an inconsistent rule learned by NeoDISCIPLE, together with the examples from which the rule has been learned. The elicitation process is characterized by a guided interaction with the human expert, who is asked to make relevant distinctions pertaining to concepts appearing in the positive and negative examples of the rule. The first method elicits concept properties through a goal-driven property transfer from one concept to another, and the second one elicits concepts using a goal-driven conceptual clustering. In both cases the elicited knowledge is used to improve the inconsistent rule while simultaneously extending the representation space for learning.