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Metasearch information fusion using linearprogramming

Published online by Cambridge University Press:  08 November 2012

Gholam R. Amin ,
Ali Emrouznejad  and
Hamid Sadeghi
Gholam R. Amin
Affiliation:
Postgraduate Engineering Centre, Islamic Azad University, South Tehran Branch, Tehran, Iran. gamin@azad.ac.ir
Ali Emrouznejad
Affiliation:
Aston Business School, Aston University, Birmingham, UK
Hamid Sadeghi
Affiliation:
Department of Computer Engineering, Hashtgerd Branch, Islamic Azad University, Alborz, Iran

Abstract

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For a specific query merging the returned results from multiple search engines, in theform of a metasearch aggregation, can provide significant improvement in the quality ofrelevant documents. This paper suggests a minimax linear programming (LP) formulation forfusion of multiple search engines results. The paper proposes a weighting method toinclude the importance weights of the underlying search engines. This is a two-phaseapproach which in the first phase a new method for computing the importance weights of thesearch engines is introduced and in the second stage a minimax LP model for findingrelevant search engines results is formulated. To evaluate the retrieval effectiveness ofthe suggested method, the 50 queries of the 2002 TREC Web track were utilized andsubmitted to three popular Web search engines called Ask, Bing and Google. The returnedresults were aggregated using two exiting approaches, three high-performance commercialWeb metasearch engines and our proposed technique. The efficiency of the generated listswas measured using TREC-Style Average Precision (TSAP). The new findings demonstrate thatthe suggested model improved the quality of merging considerably.

Keywords

Linear programmingsearch enginemetasearchinformation fusioninformation retrieval

Information

Type
Research Article
Information
RAIRO - Operations Research , Volume 46 , Issue 4 , October 2012 , pp. 289 - 303
Copyright
© EDP Sciences, ROADEF, SMAI, 2012

References

Références

Akritidis, L., Katsaros, D. and Bozanis, P., Effective rank aggregation for metasearching. J. Syst. Soft. 84 (2011) 130–143. Google Scholar
Amin, G.R. and Emrouznejad, A., An extended minimax disparity to determine the OWA operator weights. Comput. Ind. Eng. 50 (2006) 312–316. Google Scholar
Amin, G.R. and Emrouznejad, A., Finding relevant search engines results : a minimax linear programming approach. J. Oper. Res. Soc. 61 (2010) 1144–1150. Google Scholar
Amin, G.R. and Sadeghi, H., Application of Prioritized Aggregation Operators in Preference Voting. Int. J. Intell. Syst. 25 (2010) 1027–1034. Google Scholar
R.A. Baeza-Yates and B. Ribeiro-Neto, Modern information retrieval : the concepts and technology behind search, 2nd edition. ACM Press Books (2010).
Bar-Ilan, J., Mat-Hassan, M. and Levene, M., Methods for comparing rankings of search engine results. Comput. Netwo. 50 (2006) 1448–63. Google Scholar
Deka, S.K. and Lahkar, N., Performance evaluation and comparison of the five most used search engines in retrieving web resources. Online Inf. Rev. 34 (2010) 757–771. Google Scholar
Diaz, E.D., De, A. and Raghavan, V., A comprehensive OWA-based framework for result merging in metasearch. Lect. Notes Comput Sci. 3642 (2005) 193–201. Google Scholar
De, A., Diaz, E.D. and Raghavan, V., A fuzzy search engine weighted approach to result merging for metasearch. Lect. Notes Comput Sci. 4482 (2007) 95–102. Google Scholar
Emrouznejad, A., MP-OWA : The Most Preferred OWA Operator. Knowl-Based Syst. 21 (2008) 847–851. Google Scholar
M. Farah and D. Vanderpooten, An outranking approach for rank aggregation in information retrieval. Proceedings of the 30th ACM SIGIR conference on Research and development in information retrieval. Amsterdam, The Netherlands (2007) 591–598.
Herrera-Viedma, E., J. Lopez Gijon, S. Alonso, J. Vilchez, C. Garcia and L. Villen, Applying Aggregation Operators for Information Access Systems : An Application in Digital Libraries. Int. J. Intell. Syst. 23 (2008) 1235–1250. Google Scholar
Lu, Y., Meng, W., Shu, L., Yu, C. and Liu, K.L., Evaluation of Result Merging Strategies for Metasearch Engines. Lect. Notes Comput. Sci. 3806 (2005) 53–66. Google Scholar
Meng, W., Yu, C. and Liu, K.L., Building efficient and effective metasearch engines. ACM Comput. Surv. 34 (2002) 48–89. Google Scholar
Sadeghi, H., Assessing metasearch engine performance. Online Inf. Rev. 33 (2009) 1058–1065. Google Scholar
Sadeghi, H., Empirical challenges and solutions in construction of a high-performance metasearch engine. Online Inf. Rev. 36 (2012) 713–723. Google Scholar
S. Shekhar, K.V. Arya, R. Agarwal and R. Kumar, A WEBIR Crawling Framework for Retrieving Highly Relevant Web Documents : Evaluation Based on Rank Aggregation and Result Merging Algorithms, International Conference on Computational Intelligence and Communication Networks (CICN) (2011) 83–88.
Shokouhi, M. and Si, L., Federated Search. Found. Trends Inf. Retr. FTIR 5 (2011) 1–102. Google Scholar
Silva, T.P.C., De Moura, E.S., Cavalcanti, J.M.B., A.S. Da Silva, M.G. De Carvalho and M.A. Gonçalves, An evolutionary approach for combining different sources of evidence in search engines. Inform. Syst. 34 (2009) 276–289. Google Scholar
E. Voorhees, Overview of TREC 2002, in Proceedings of the 11th Text REtrieval Conference (TREC), Gaithersburg, MD, USA (2002) 1–15.
Wu, S., Applying statistical principles to data fusion in information retrieval. Expert Syst. Appl. 36 (2009) 2997–3006. Google Scholar
Wu, S., Linear combination of component results in information retrieval. Data Knowl. Eng. 71 (2012) 114–126. Google Scholar
S. Wu and F. Crestani, Data fusion with estimated weights, CIKM ’02 : Proceedings of the eleventh international conference on Information and knowledge management (2002) 648–651.
Wu, S., Bi, Y., Zeng, X. and Han, L., The Experiments with the Linear Combination Data Fusion Method in Information Retrieval. Lect. Notes Comput. Sci. 4976 (2008) 432–437. Google Scholar
Yao, J.T., Raghavan, V. and Wu, Z., Web information fusion : A review of the state of the art. Inform Fusion 9 (2008) 446–449. Google Scholar
X.S. Xie and G. Zhang, Study of Optimizing the Merging Results of Multiple Resource Retrieval Systems by a Particle Swarm Algorithm. International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC) (2011) 39–42.
Zhou, S., Xu, M. and Guan, J., LESSON : A system for lecture notes searching and sharing over Internet. J. Syst. Soft. 83 (2010) 1851–1863. Google Scholar
Zhou, G.T., Ting, K.M., Liu, F.T. and Yin, Y., Relevance feature mapping for content-based multimedia information retrieval. Pattern Rec. 45 (2012) 1707–1720 Google Scholar