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Granular Knowledge Representation and Inference Using Labels and Label Expressions

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2 Author(s)
Jonathan Lawry ; Department of Engineering Mathematics, University of Bristol, Bristol, U.K. ; Yongchuan Tang

This paper is a review of the label semantics framework as an epistemic approach to modeling granular information represented by linguistic labels and label expressions. The focus of label semantics is on the decision-making process that a rational communicating agent must undertake in order to establish which available labels can be appropriately used to describe their perceptual information in such a way as they are consistent with the linguistic conventions of the population. As such, it provides an approach to characterizing the relationship between labels and the underlying perceptual domain which, we propose, lies at the heart of what is meant by information granules. Furthermore, it is then shown that there is an intuitive relationship between label semantics and prototype theory, which provides a clear link with Zadeh's original conception of information granularity. For information propagation, linguistic mappings are introduced, which provide a mechanism to infer labeling information about a decision variable from the available labeling information about a set of input variables. Finally, a decision-making process is outlined whereby from linguistic descriptions of input variables, we can infer a linguistic description of the decision variable and, where required, select a single expression describing that variable or a single estimated value.

Published in:

IEEE Transactions on Fuzzy Systems  (Volume:18 ,  Issue: 3 )