Interval Rough Mereology for Approximating Hierarchical Knowledge

  • Authors:

  • Pavel Klinov;Lawrence J. Mazlack

  • Affiliations:

  • Applied Computational Intelligence Laboratory, University of Cincinnati, Cincinnati, OH 45221-0030,;Applied Computational Intelligence Laboratory, University of Cincinnati, Cincinnati, OH 45221-0030,

  • Venue:
  • RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
  • Year:
  • 2009

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Abstract

The paper proposes an approach based on Rough Mereology to approximating hierarchical relationships between imprecise concepts in knowledge representation systems. The approach employs Interval Analysis to capture the imprecision caused by the granularity of knowledge. Interval rough inclusion functions are defined. It is demonstrated that they can be effectively used to compute the IS-A relationships by measuring the inclusion of one approximated concept into another. It is shown that the functions are superior to the previously suggested in literature.