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JACIII Vol.11 No.1 pp. 71-78
doi: 10.20965/jaciii.2007.p0071
(2007)

Paper:

An Inference Method for Fuzzy Quantified Natural Language Propositions Based on New Interpretation of Truth Qualification

Wataru Okamoto*, Shun’ichi Tano**, Toshiharu Iwatani***,
and Atsushi Inoue****

*Yokohama, Kanagawa 240-0042, Japan

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

***Kobe Steel Ltd., Kobe, Hyogo 651-8585, Japan

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

Received:
December 12, 2005
Accepted:
May 31, 2006
Published:
January 20, 2007
Keywords:
natural language, fuzzy inference, fuzzy quantifiers, truth qualifiers, modifiers
Abstract

In this paper, we propose a method that affects inference results leading to a new interpretation of a truth qualification by adding a weight attribute to truth qualified fuzzy sets. With this method, we can obtain different inference results depending on the truth qualifiers by transforming a statement with fuzzy quantified and truth qualified natural language propositions. We applied our method to four examples transforming a fuzzy predicate of the natural language propositions and showed an effectiveness of the method.

Cite this article as:
Wataru Okamoto, Shun’ichi Tano, Toshiharu Iwatani, and
and Atsushi Inoue, “An Inference Method for Fuzzy Quantified Natural Language Propositions Based on New Interpretation of Truth Qualification,” J. Adv. Comput. Intell. Intell. Inform., Vol.11, No.1, pp. 71-78, 2007.
Data files:
References
  1. [1] L. A. Zadeh, “PRUF-A meaning representation language for natural language,” International Journal of Man Machine Studies, Vol.10, pp. 395-460, 1978.
  2. [2] L. A. Zadeh, “A Theory of Approximate Reasoning,” Machine Intelligence, Vol.9, New York: Halstead Press, pp. 149-194, 1979.
  3. [3] L. A. Zadeh, “A Computational Approach to Fuzzy Quantifiers in Natural Languages,” Computers and Mathematics with Applications, Vol.9, pp. 149-184, 1983.
  4. [4] D. Dubois and H. Prade, “Gradual Inference Rules in Approximate Reasoning,” Information Sciences, Vol.61, pp. 103-122, 1992.
  5. [5] W. Okamoto, S. Tano, T. Iwatani, and A. Inoue, “Inference Method for Natural Language Propositions involving Fuzzy Quantifiers in FLINS,” Proceedings of Third IEEE International Conference on Fuzzy Systems (FUZZ-IEEE’94), Orlando, pp. 1023-1028, 1994.
  6. [6] W. Okamoto, S. Tano, T. Iwatani, and A. Inoue, “Inference Method for Truth-Qualified Natural Language Propositions involving Fuzzy Quantifiers,” Proceedings of Second European Congress on Intelligent Techniques and Soft Computing (EUFIT’94), Aachen, pp. 1594-1598, 1994.
  7. [7] W. Okamoto, S. Tano, T. Iwatani, and A. Inoue, “Inference Method for Natural Language Propositions Involving Fuzzy Quantifiers,” Japanese Journal of Fuzzy Theory and Systems, Vol.7, No.4, pp. 509-536, 1995.
  8. [8] W. Okamoto, S. Tano, A. Inoue, and R. Fujioka, “Constrains on Natural Language Propositions Involving Fuzzy Quantifiers by Truth-Qualification and Inference Method for the Propositions,” Journal of Japan Society for Fuzzy Theory and Systems, Vol.8, No.3, pp. 519-531, 1996 (in Japanese).
  9. [9] W. Okamoto, S. Tano, A. Inoue, and R. Fujioka, “Inference Method for Fuzzy Quantified and Truth Qualified Natural Language Propositions,” Electronics and Communications in Japan, Part 3, Vol.83, No.2, pp. 22-43, 2000.
  10. [10] S. Tano, W. Okamoto, T. Iwatani, A. Inoue, and R. Fujioka, “Fuzzy Natural Language Communication System – FLINS: Concept and Conversation Examples,” Proceedings of Fourth IEEE International Conference on Fuzzy Systems and Second International Fuzzy Engineering Symposium (FUZZ-IEEE/IFES’95), Yokohama, pp. 1039-1044, 1995.
  11. [11] R. Fujioka, S. Tano, W. Okamoto, A. Inoue, and T. Iwatani, “Treatment of Fuzziness in Natural Language by Fuzzy Lingual System – FLINS,” Proceedings of Fourth IEEE International Conference on Fuzzy Systems and Second International Fuzzy Engineering Symposium (FUZZ-IEEE/IFES’95), Yokohama, pp. 1045-1050, 1995.
  12. [12] W. Okamoto, S. Tano, A. Inoue, and R. Fujioka, “A Generalized Inference Method for Natural Language Propositions Involving Fuzzy Quantifiers and Truth Qualifiers,” Proceedings of 14th IEEE International Conference on Fuzzy Systems (FUZZ-IEEE’05), Reno, pp. 672-677, 2005.

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