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Considering Semantic Ambiguity and Indistinguishability for Values of Membership Attribute in Possibility-Based Fuzzy Relational Models

  • Michinori Nakata
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3066)

Abstract

A possibility-based fuzzy relational model is proposed under considering semantic ambiguity and indistinguishability for values of membership attribute. In order to eliminate the semantic ambiguity, a membership attribute is attached to every attribute. This clarifies where each value of membership attributes comes from. What the values of membership attributes mean depends on the property of those attributes. In order to eliminate the indistinguishability for values of membership attribute, these values are expressed by possibility distributions on the interval [0,1]. This clarifies what effects an imprecise data value allowed for an attribute has on its value of membership attribute. Therefore, there is no semantic ambiguity and no indistinguishability for the values of membership attributes in the possibility-based fuzzy relational model.

Keywords

Query Processing Conjunctive Normal Form Imperfect Information Possibility Distribution Possibility Theory 
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 2004

Authors and Affiliations

  • Michinori Nakata
    • 1
  1. 1.Faculty of Management and Information ScienceJosai International UniversityChibaJapan

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