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EXPECT: Intelligent support for knowledge base refinement

Life Cycle and Methodologies Refinement
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 723)

Abstract

Effective knowledge acquisition amounts to having good sources of expectations that can provide guidance about what knowledge needs to be acquired from users. Current approaches to knowledge acquisition often rely on strong models of the problem-solving method used in the task domain to form expectations. These methods are often implicit in the tool, which is a strong limitation for their use in different domains. Additionally, these tools require an understanding of the method to be used that most experts find difficult to overcome. In this paper we present EXPECT, a novel approach to knowledge acquisition based on the EES architecture that forms expectations based on the current knowledge contained in the system about the task, and are not hard-coded in the tool. We show how the explicit representation of domain principles and its relation to compiled procedural knowledge enables a system to form expectations as to what knowledge is missing or incorrect. This capability coupled with a dialogue-based explanation facility makes communication with the knowledge acquisition tool more natural to domain experts.

Keywords

Knowledge Base Expert System Domain Model Knowledge Acquisition Procedural Knowledge 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1993

Authors and Affiliations

  1. 1.Information Sciences Institute and Department of Computer ScienceUniversity of Southern CaliforniaMarina del ReyUSA

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