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JACIII Vol.11 No.5 pp. 502-510
doi: 10.20965/jaciii.2007.p0502
(2007)

Paper:

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A Generalized Inference Method for Fuzzy Quantified and Truth-Qualified Natural Language Propositions

Wataru Okamoto*, Shun’ichi Tano**, Atsushi Inoue***,
and Ryosuke Fujioka****

*Yokohama, Kanagawa 240-0042, Japan

**University of Electro-Communications, Chofu, Tokyo 182-8585, Japan

***Eastern Washington University, Cheney, WA 99004-2412, USA

****Kobe Sogo Sokki Co., Ltd, Kobe, Hyogo 650-0012, Japan

Received:
December 19, 2006
Accepted:
April 25, 2007
Published:
June 20, 2007
Creative Commons License
Keywords:
natural language, fuzzy inference, fuzzy quantifier, truth qualifier, modifier
Abstract
We propose a generalized inference method for constructing natural language communication. The method is used to obtain fuzzy quantifier Q’  when “QA are F is τ →Q’ (m’A) are mF is m’’τ” is inferred (Q, Q’: fuzzy quantifiers, A: fuzzy subject, m, m’, m’’: modifiers, F: fuzzy predicate, τ: truth qualifier). We show that Q’  is resolved step by step for a non-increasing type (few, ...) and a non-decreasing type (most, ...).
Cite this article as:
W. Okamoto, S. Tano, A. Inoue, and R. Fujioka, “A Generalized Inference Method for Fuzzy Quantified and Truth-Qualified Natural Language Propositions,” J. Adv. Comput. Intell. Intell. Inform., Vol.11 No.5, pp. 502-510, 2007.
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References
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