Using transformations to improve semantic matching

  • Authors:

  • Peter Z. Yeh;Bruce Porter;Ken Barker

  • Affiliations:

  • University of Texas, Austin, Austin, TX;University of Texas, Austin, Austin, TX;University of Texas, Austin, Austin, TX

  • Venue:
  • Proceedings of the 2nd international conference on Knowledge capture
  • Year:
  • 2003

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Abstract

Many AI tasks require determining whether two knowledge representations encode the same knowledge. Solving this matching problem is hard because representations may encode the same content but differ substantially in form. Previous approaches to this problem have used either syntactic measures, such as graph edit distance, or semantic knowledge to determine the "distance" between two representations. Although semantic approaches outperform syntactic ones, previous research has focused primarily on the use of taxonomic knowledge. We show that this is not enough because mismatches between representations go largely unaddressed. In this paper, we describe how transformations can augment existing semantic approaches to further improve matching. We also describe the application of our approach to the task of critiquing military Courses of Action and compare its performance to other leading algorithms.