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Frontiers of Computer Science

Front. Comput. Sci.    2018, Vol. 12 Issue (1) : 163-176     https://doi.org/10.1007/s11704-016-5560-0
RESEARCH ARTICLE |
Strength Pareto fitness assignment for pseudo-relevance feedback: application to MEDLINE
Ilyes KHENNAK(), Habiba DRIAS
Laboratory for Research in Artificial Intelligence, Computer Science Department, University of Sciences and Technology Houari Boumediene (USTHB), Algiers 16111, Algeria
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Abstract

Because of users’ growing utilization of unclear and imprecise keywords when characterizing their information need, it has become necessary to expand their original search queries with additional words that best capture their actual intent. The selection of the terms that are suitable for use as additional words is in general dependent on the degree of relatedness between each candidate expansion term and the query keywords. In this paper, we propose two criteria for evaluating the degree of relatedness between a candidate expansion word and the query keywords: (1) co-occurrence frequency, where more importance is attributed to terms occurring in the largest possible number of documents where the query keywords appear; (2) proximity, where more importance is assigned to terms having a short distance from the query terms within documents. We also employ the strength Pareto fitness assignment in order to satisfy both criteria simultaneously. The results of our numerical experiments on MEDLINE, the online medical information database, show that the proposed approach significantly enhances the retrieval performance as compared to the baseline.

Keywords information retrieval      query expansion      pseudorelevance feedback      proximity      multi-objective optimization      Pareto dominance      MEDLINE     
Corresponding Authors: Ilyes KHENNAK   
Just Accepted Date: 28 September 2016   Online First Date: 08 December 2017    Issue Date: 12 January 2018
 Cite this article:   
Ilyes KHENNAK,Habiba DRIAS. Strength Pareto fitness assignment for pseudo-relevance feedback: application to MEDLINE[J]. Front. Comput. Sci., 2018, 12(1): 163-176.
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http://journal.hep.com.cn/fcs/EN/10.1007/s11704-016-5560-0
http://journal.hep.com.cn/fcs/EN/Y2018/V12/I1/163
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Ilyes KHENNAK
Habiba DRIAS
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