Personalized Search Service in Semantic Web


The KIPS Transactions:PartB , Vol. 13, No. 5, pp. 533-540, Oct. 2006
10.3745/KIPSTB.2006.13.5.533,   PDF Download:

Abstract

The semantic web environment promise semantic search of heterogeneous data from distributed web page. Semantic search would result in an overwhelming number of results for users is increased, therefore elevating the need for appropriate personalized ranking schemes. Culture Finder helps semantic web agents obtain personalized culture information. It extracts meta-data for each web page(culture news, culture performance, culture exhibition), perform semantic search and compute result ranking point to base user profile. In order to work efficient, Culture Finder uses five major technique: Machine learning technique for generating user-profile from user search behavior and meta-data repository, an efficient semantic search system for semantic web agent, query analysis for representing query and query result, personalized ranking method to provide suitable search result to user, upper ontology for generating meta-data. In this paper, we also present the structure used in the Culture Finder to support personalized search service.


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Cite this article
[IEEE Style]
J. M. Kim and Y. T. Park, "Personalized Search Service in Semantic Web," The KIPS Transactions:PartB , vol. 13, no. 5, pp. 533-540, 2006. DOI: 10.3745/KIPSTB.2006.13.5.533.

[ACM Style]
Je Min Kim and Young Tack Park. 2006. Personalized Search Service in Semantic Web. The KIPS Transactions:PartB , 13, 5, (2006), 533-540. DOI: 10.3745/KIPSTB.2006.13.5.533.