Quantitative Common Sense Estimation System and its Application for Membership Function Generation

Yuta Hayakawa; Masafumi Hagiwara

doi:10.20965/jaciii.2014.p0856

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JACIII Vol.18 No.5 pp. 856-864

doi: 10.20965/jaciii.2014.p0856

(2014)

Paper:

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Quantitative Common Sense Estimation System and its Application for Membership Function Generation

Yuta Hayakawa and Masafumi Hagiwara

Department of Information and Computer Science, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan

Received:

December 11, 2013

Accepted:

July 7, 2014

Published:

September 20, 2014

Keywords:

quantitative common sense, web, data mining, knowledge acquisition, membership function

Abstract

Systems capable of autonomous thinking are sometimes required to cope with unanticipated situations. An important issue in this context is knowledge – especially common sense – acquisition. In this paper, we propose novel quantitative common sense estimation methods and apply them to an automatic membership function generation system. Our proposed system estimates threshold values corresponding to large and small for various kinds of objectattribute sets to form membership functions, where it attempts to relate each object to its corresponding impression. Two methods are proposed in this paper. The first, Method-1, obtains data from the top 1,000 snippets through a web search and estimates the global and local tendencies by clustering them. The second, Method-2, uses the number of hits from a web search together with parts of the results obtained through Method-1. In addition, we devise several techniques to eliminate unnecessary information in the retrieved web pages. We also carried out experiments that verified the effectiveness of our proposed methods and the method combining those two.

Cite this article as:

Y. Hayakawa and M. Hagiwara, “Quantitative Common Sense Estimation System and its Application for Membership Function Generation,” J. Adv. Comput. Intell. Intell. Inform., Vol.18 No.5, pp. 856-864, 2014.

Data files:

References

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[3] P. Singh, “The open mind common sense project,” KurzweilAI.net, 2002.
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[5] R. Speer, C. Havasi, and H. Lieberman, “AnalogySpace: Reducing the Dimensionality of Common Sense Knowledge,” Artificial Intelligence, pp. 548-553, 2008.
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[12] M. Shirakawa, K. Nakayama, T. Hara, and S. Nishio, “Concept Vectorization Methods using Wikipedia Category Network (in Japanese),” IPSJ SIG Notes, Vol.56, pp. 89-96, 2008.
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This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] J.McCarthy, “Artificial intelligence, logic and formalizing common sense,” Philosophical logic and artificial intelligence, pp. 161-190, 1989.

[2] [2] D. B. Lenat, “CYC: a large-scale investment in knowledge infrastructure,” Commun., ACM, Vol.38, pp. 33-38, November 1995.

[3] [3] P. Singh, “The open mind common sense project,” KurzweilAI.net, 2002.

[4] [4] C. Havasi, J. Pustejovsky, R. Speer, and H. Lieberman, “Digital Intuition: Applying Common Sense Using Dimensionality Reduction,” Intelligent Systems, IEEE, Vol.24, No.4, pp. 24-35, Jul.-Aug. 2009.

[5] [5] R. Speer, C. Havasi, and H. Lieberman, “AnalogySpace: Reducing the Dimensionality of Common Sense Knowledge,” Artificial Intelligence, pp. 548-553, 2008.

[6] [6] C. Fellbaum, “WordNet,” Theory and Applications of Ontology: Computer Applications, pp. 231-243, 2010.

[7] [7] EDR Electronic Dictionary (in Japanese),
http://www2.nict.go.jp/out-promotion/techtransfer/EDR/J index.html. [Accessed February 1, 2012]

[8] [8] D. Lenat, G. Miller, and T. Yokoi, “CYC, WordNet, and EDR: critiques and responses,” Communications of the ACM, Vol.38, No.11, pp. 45-48, 1995.

[9] [9] C. Elkan and R. Greiner, “Building large knowledge-based systems: representation and inference in the Cyc project,” Artificial Intelligence, Vol.61, No.1, pp. 41-52, 2006.

[10] [10] Y. Sato, H. Watabe, and T. Kawaoka, “Construction of Commonsense Quantitative Judgment Mechanism : Extension of Relative Evaluation about Quantity (written in Japanese),” FIT : Forum on Information Technology, Vol.6, No.2, pp. 283-286, 2007.

[11] [11] M. Ohta, S. Shimada, T. Iida, and T. Kawaoka, “Quantification in Artificial Intelligence with Quantitative Common Sense (in Japanese),” IEICE Technical Report, Artificial Intelligence and Knowledge-Based Processing, Vol.93, No.493, pp. 17-24, 1994.

[12] [12] M. Shirakawa, K. Nakayama, T. Hara, and S. Nishio, “Concept Vectorization Methods using Wikipedia Category Network (in Japanese),” IPSJ SIG Notes, Vol.56, pp. 89-96, 2008.

[13] [13] Collaborative investigation between Kyoto University and Nippon Telegraph and Telephone Corporation, “MeCab: Yet Another Partof-Speech and Morphological Analyzer (written in Japanese),”
http://mecab.sourceforge.net/. [Accessed February 1, 2012]

[14] [14] T. Kan, “‘EXCELtoukei’ notamenotoukeibunsekinohon (Book of statistical analysis for Excel statistics) (in Japanese),” Esumi, 2006.

[15] [15] Yahoo!Developer’s network (written in Japanese),
http://developer.yahoo.co.jp/. [Accessed February 1, 2012]

[16] [16] C. Tropea, A. Yarin, and J. Foss, “Springer handbook of experimental fluid mechanics,” Vol.1, Springer Verlag, 2007.