Linguistic modelling and information coarsening based on prototype theory and label semantics

Tang Yongchuan, J Lawry

Research output: Contribution to journalArticle (Academic Journal)peer-review

21 Citations (Scopus)

Abstract

The theory of prototypes provides a new semantic interpretation of vague concepts. In particular, the calculus derived from this interpretation results in the same calculus as label semantics proposed by Lawry. In the theory of prototypes, each basic linguistic label L has the form ‘about P’, where P is a set of prototypes of L and the neighborhood size of the underlying concept is described by the word ‘about’ which represents a probability density function δ on [0,+∞). In this paper we propose an approach to vague information coarsening based on the theory of prototypes. Moreover, we propose a framework for linguistic modelling within the theory of prototypes, in which the rules are concise and transparent. We then present a linguistic rule induction method from training data based on information coarsening and data clustering. Finally, we apply this linguistic modelling method to some benchmark time series prediction problems, which show that our linguistic modelling and information coarsening methods are potentially powerful tools for linguistic modelling and uncertain reasoning.
Translated title of the contributionLinguistic modelling and information coarsening based on prototype theory and label semantics
Original languageEnglish
Pages (from-to)1177 - 1198
JournalInternational Journal of Approximate Reasoning
Volume50
Publication statusPublished - 2010

Bibliographical note

Author of Publication Reviewed: Yongchuan Tang, Jonathan Lawry
Publisher: Springer

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