Fuzzy Information Retrieval Method Based on Fuzzy-Valued Concept Networks

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Abstract:

In order to reduce the time of fuzzy inference, the relevant matrices and the relationship matrices are used to constitute the fuzzy-valued concept networks. The elements of a relevant matrix represent the relevant degrees between concepts. The elements of a relationship matrix represent the relevant relationships between concepts. Fuzzy positive association relationship or fuzzy negative association relationship are used for formulating users queries in order to increase the flexibility of fuzzy information retrieval systems. Expanding the fuzzy-valued concept network architecture to the Internet environment, we propose a fuzzy information retrieval method based on the network-type fuzzy-valued concept network and it can be relatively more effective information retrieval in the distributed network

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506-511

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February 2014

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] D. Lucarella,R. Morara. FIRST: Fuzzy Information Retrieval System[J]. Acm Transactions on Modeling and Computer Simulation, 1991, Vol. 17(2): 81-91.

DOI: 10.1177/016555159101700202

Google Scholar

[2] S.M. Chen Y.J. Hoang C.H. Lee. Fuzzy Information Retrieval Based on Multi-relationship Fuzzy Concept Networks[C]. Fuzzy Set and Systems Trans. 140. Amsterdam: Elsevier, 2003: 183-205.

DOI: 10.1016/s0165-0114(02)00464-5

Google Scholar

[3] Lowen R. Convex Fuzzy Sets[J]. Fuzzy sets and systems, 1980, Vol. 3(3): 291-310.

DOI: 10.1016/0165-0114(80)90025-1

Google Scholar

[4] Spink. A, Wolfram. D, Jansen. B, et al. Searching the Web: The public and their queries[J]. Journal of the American Society for Information Science, 2001, Vol. 52(3): 226~234.

DOI: 10.1002/1097-4571(2000)9999:9999<::aid-asi1591>3.0.co;2-r

Google Scholar

[5] R.Y.K. Lau,Y. Li, and Y. Xu. Mining Fuzzy Domain Ontology From Textual Databases[C]. In WI'07: Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, 2007: 156–162.

DOI: 10.1109/wi.2007.20

Google Scholar

[6] Yu Yangxin. Semantic Information Retrieval Study Based on Knowledge Reasoning[J]. Journal of Information, 2008, Vol. 27(11): 78-80.

Google Scholar

[7] Zhu Lijun, Tao Lan, Liu Hui. Caculation of the concept similarity on domain ontology[J]. Journal of South China University of Technology(Natural Science Edition), 2004, Vol. 32(11): 147-159.

Google Scholar

[8] Rodriguez M, Egenhofer M. Determining semantic similarity among entity classes from different ontologies[J]. IEEE Transactions on Knowledge and Data Engineering, 2003, Vol. 15(2): 442-456.

DOI: 10.1109/tkde.2003.1185844

Google Scholar