Learning domain theories using abstract background knowledge

  • Peter Clark
  • Stan Matwin
Position Papers Learning from Time Dependent Data
Part of the Lecture Notes in Computer Science book series (LNCS, volume 667)


Substantial machine learning research has addressed the task of learning new knowledge given a (possibly incomplete or incorrect) domain theory, but leaves open the question of where such domain theories originate. In this paper we address the problem of constructing a domain theory from more general, abstract knowledge which may be available. The basis of our method is to first assume a structure for the target domain theory, and second to view background knowledge as constraints on components of that structure. This enables a focusing of search during learning, and also produces a domain theory which is explainable with respect to the background knowledge. We evaluate an instance of this methodology applied to the domain of economics, where background knowledge is represented as a qualitative model.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1993

Authors and Affiliations

  • Peter Clark
    • 1
  • Stan Matwin
    • 2
  1. 1.Knowledge Systems LaboratoryNational Research CouncilOttawaCanada
  2. 2.Ottawa Machine Learning GroupComputer Science University of OttawaOttawaCanada

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