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New Technology of Library and Information Service  2006, Vol. 1 Issue (5): 27-30    DOI: 10.11925/infotech.1003-3513.2006.05.07
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Multi-Agent Intelligent Retrieval System Model Research Based on Ontology
Zhao Wei1  Sun Wandong2
1(Network Center of Ludong University, Yantai 264025, China)
2(Library of Ludong University, Yantai 264025, China)
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In view of the existing limitation of search engines in the information retrieval, this paper proposes multi-agent intelligent retrieval system model based on Ontology, and gives the system structure, workflow, function description of the model.  Intelligent Agent utilizes ontology knowledge to normalize retrieval request information, which can improve the accuracy rate and cover rate of retrieval. Agents collaborate divisibly to finish information retrieval and automatic update service, and embody the intellectualization and individuality of system,etc. This model can provide foundation for realizing the highly effective intelligent retrieval system research.

Key wordsOntology      Multi-Agent      Intelligent retrieval     
Received: 27 February 2006      Published: 25 May 2006


Corresponding Authors: Zhao Wei     E-mail:
About author:: Zhao Wei,Sun Wandong

Cite this article:

Zhao Wei,Sun Wandong . Multi-Agent Intelligent Retrieval System Model Research Based on Ontology. New Technology of Library and Information Service, 2006, 1(5): 27-30.

URL:     OR Arprez, A. Gmez-Prez, A. Lozano, S. Pinto: (ONTO)2Agent: An ontology-based WWW broker to select ontologies. In Proceedings of the ECAI-98 Workshop on Applications of Ontologies and PSMs, Brighton. England. August 1998:16-24
2Ontobroker. Jan.04,2006)
3SKC. Jan.15,2006)
4N. Guarino, C. Masolo, and G Vetere: Ontoseek: Content-based Access to the Web, IEEE Intelligent Systems,  1999, 14(3): 70-80
5Joachims T, Freitag D, Mitchell T, Webwatcher: A tour guide for the World Wide Web. In Proc. IJCAI-97 [EB/OL]. Jan.13,2006)
6Buckly C.Implementation of the smart information retrieval system. Department f computer Science, Cornell University,Technical Report TR85-686, May 1985. Jan.04,2006)
7Doorenbos R B, Etzioni O and Weld D S,A scalable comparison- shopping agent for the World-Wide Web[EB/OL], 1997. Dec.25,2005)
8Pazzani M,Muramatsu J ,Billus D. Syskill & Webert: Indentifying Interesting Web Sites. In Proceedings of the Thirteen Nacional Conference on Artificial Intelligence, Portland: AAAI Press, 1996.54-61
10Uschold M, Gruninger M. Ontologies: Principles, Methods and Applications. The Knowledge Engineering Review,1996,11(2):93-155
11Wooldridge M,Jennings N R.Intelligent agents:Theory and Practice.The knowledge Engineering Review,1995,10(2):115-152
12Lange D B. Mobile Object and Mobile Agents:The Future of Distributed Computing[EB/OL]. (Accessed Dec.20,2005)
13Katia P Sycara. Multi-agent Systems,AI Magazine,1998(summer):79-92
15张云勇.移动Agent及其应用. 北京:清华大学出版社,20027-33
16史忠植.知识工程. 北京:清华大学出版社,1988

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