Concept Similarity and Cosine Similarity Result Merging Approaches in Metasearch Engine

  • K. Srinivas
  • A. Govardhan
  • V. Valli Kumari
  • P. V. S. Srinivas
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 150)


Metasearch engines provide a uniform query interface for Internet users to search for information. Depending on users need, they select relevant sources and map user queries into the target search engines, subsequently merging the results. In this paper, we have proposed a metasearch engine, which have two unique steps (1) searching through surface and deep web, and (2) Ranking the results through the designed ranking algorithm. Initially, the query given by the user is given to the surface and deep search engines. Here, the surface search engines like Google, Bing and Yahoo are considered. At the same time, the deep search engine such as, Infomine, Incywincy and CompletePlanet are considered. The proposed method will use two distinct algorithms for ranking the search results, which are concept similarity and cosine similarity.


Metasearch engine Concept Cosine similarity Deep web Surface web 


  1. 1.
    Jianting L (2011) Processing and fusion of meta-search engine retrieval results. In: International conference on electric information and control engineering, pp 3442–3445Google Scholar
  2. 2.
    Ghaderi MA, Yazdani N, Moshiri B (2010) A social network-based metasearch engine. International symposium on telecommunications, pp 744–749Google Scholar
  3. 3.
    Krüpl B, Baumgartner R (2009) Flight meta-search engine with metamorph. Trans ACM J 1069–1070Google Scholar
  4. 4.
    Bravo-Marquez F, L’Huillier G, R′ios SA, Vel′asquez JD, Guerrero LA (2010) DOCODE-Lite: a meta-search engine for document similarity retrieval. In: Proceedings of the 14th international conference on knowledge-based and intelligent information and engineering systems, pp 93–102Google Scholar
  5. 5.
    Rasolofo Y, AbbaFci F, Savoy J (2001) Approaches to collection selection and results merging for distributed information retrieval”. In: Proceedings of the tenth international conference on information and knowledge management, pp 191–198Google Scholar
  6. 6.
    Aslam JA, Montague M, (2001) Models for metasearch. In: Proceedings of the 24th annual international ACM SIGIR conference on research and development in information retrieval, pp 276–284Google Scholar
  7. 7.
    Zaka B (2009) Empowering plagiarism detection with a web services enabled collaborative network. J Inform Sci Eng 25:1391–1403Google Scholar
  8. 8.
    Selberg E, Etzioni O (1997) The metacrawler architecture for resource aggregation on the web. IEEE Expert J 11–14Google Scholar
  9. 9.
    Keyhanipour AH, Moshiri B, Kazemian M, Piroozmand M, Lucas C (2007) Aggregation of web search engines based on users’preferences in WebFusion. Knowl-Based Sys 20:321–328Google Scholar
  10. 10.
    Keyhanipour AH, Moshiri B, Piroozmand M, Lucas C (2006) WebFusion: fundamentals and principals of a novel meta search engine. IJCNN’2006, pp 4126–4131Google Scholar
  11. 11.
    Smyth B, Freyne J, Coyle M, Briggs P, Balfe E, Building T (2003) I-spy: anonymous, community-based personalization by collaborative web search. In: Proceedings of the 23rd SGAI international conference on innovative techniques and applications of artificial intelligence, pp 367–380Google Scholar
  12. 12.
    Chignell MH, Gwizdka J, Bodner RC (1999) Discriminating meta-search: a framework for evaluation. Inform Proces Manage 35:337–36Google Scholar
  13. 13.
    Dreilinger D, Howe AE (1997) Experiences with selecting search engines using metasearch. ACM Trans Inform Sys 15:195–222Google Scholar
  14. 14.
    Jansen BJ, Spink A, Koshman S (2007) Web searcher interaction with the metasearch engine. J Am Soc for Inform Sci Technol 58:744–755Google Scholar
  15. 15.
    Howe AE, Dreilinger D (1997) SavvySearch: a meta-search engine that learns which search engines to query. AI Mag 18Google Scholar
  16. 16.
    Gauch S, Wang G, Gomez M (1996) ProFusion: intelligent fusion from multiple, distributed search engines. J Univ Comput Sci 2Google Scholar
  17. 17.
    Selberg E, Etzioni O (1997) The MetaCrawler architecture for resource aggregation on the Web. IEEE Expert 12:11–1Google Scholar
  18. 18.
    Buzikashvili N (2002) Metasearch: properties of common document distributions. Lect Notes Comput Sci 2569:226–231Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • K. Srinivas
    • 1
  • A. Govardhan
    • 2
  • V. Valli Kumari
    • 3
  • P. V. S. Srinivas
    • 4
  1. 1.Department of ITGeethanjali College of Engineering and TechnologyCheeryal(V), Keesara(M), Ranga ReddyIndia
  2. 2.Jawaharlal Nehru Technological UniversityHyderabadIndia
  3. 3.Department of CS and SEAndhra University College of Engineering, Andhra UniversityVisakhapatnamIndia
  4. 4. Department of CSEGeethanjali College of Engineering and TechnologyCheeryal(V), Keesara(M), Ranga ReddyIndia

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