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Towards painless knowledge acquisition

  • Derek Sleeman
  • Fraser Mitchell
Data Mining
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1076)

Abstract

This paper argues that there is a discernible trend in Knowledge Acquisition towards systems which are easier for the domain expert to use; such systems ask more focused questions and questions at a higher conceptual level. Two systems, REFINER+ and TIGON which illustrate this trend are described in some detail; these have been applied in the domains of patient management and diagnosis of turbine errors respectively. Other trends noted include:
  • Co-operative systems for Knowledge Acquisition/Problem Solving.

  • The re-use of existing knowledge(bases) Additionally, the relationship of the TIGON system to Data Mining is discussed; as is the inference of diagnostic rules for dynamic systems from the systems performance data.

Keywords

Expert System Fault Detection Fault Diagnosis Knowledge Acquisition Domain Expert 
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 1996

Authors and Affiliations

  • Derek Sleeman
    • 1
  • Fraser Mitchell
    • 1
  1. 1.Department of Computing Science King's CollegeThe UniversityAberdeenScotland, UK

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