Advertisement

Metasearch Engine: A Technology for Information Extraction in Knowledge Computing

  • P. Vijaya
  • Satish Chander
Chapter

Abstract

The increasing number of the Web data due to the increased amount of the digitalized standards, electronic mails, images, multimedia, and Web services, the World Wide Web rises as the cost-effective resource for releasing the data and for discovering the knowledge. Thus, the open challenges for the information retrieval efforts have received much attention among the researchers to deem the browsing as an appropriate searching procedure. To facilitate the most relevant information, it is necessary to develop a unique and extensive structural framework that offers a platter and a navigational proxy to the clients and servers. Consequently, the search engines play a vital role in contributing the users in providing the related Web pages from the Web. A Metasearch engine, in essence, is a search mechanism that sends the user query to a number of modern search engines autonomously and provides the combined outcome through their own unique page ranking technique. This chapter intends to discuss the necessity of metasearch engines, starting with a series of definitions of search engines and its classification. Further, a summary of metasearch engine is provided with the architecture and the result-merging methods. It also states several criteria that validate the stability of metasearch engines and, finally, conclude the chapter explaining the future work.

Keywords

Knowledge computing Search engine Metasearch engine Page ranking Result merging 

References

  1. 1.
    Hamdi, M. S. (2011). SOMSE: A semantic map based meta-search engine for the purpose of web information customization. Applied Soft Computing, 11(1), 1310–1321.MathSciNetCrossRefGoogle Scholar
  2. 2.
    Lawrence, S., & Giles, C. L. (1998). Context and page analysis for improved web search. IEEE Internet Computing, 38–45.Google Scholar
  3. 3.
    Terziyan, V., Shevchenko, O., & Golovianko, M. (2014). An introduction to knowledge computing. Eastern-European Journal of Enterprise Technologies, 1(2), 27–40.CrossRefGoogle Scholar
  4. 4.
    Agrawal, R., & Ramakrishnan, S. (2003). Searching with numbers. IEEE Transactions on Knowledge and Data Engineering, 15(4), 855–870.CrossRefGoogle Scholar
  5. 5.
    Antoniou, D., Plegas, Y., Tsakalidis, A., Tzimas, G., & Viennas, E. (2012). Dynamic refinement of search engines results utilizing the user intervention. Journal of Systems and Software, 85(7), 1577–1587.CrossRefGoogle Scholar
  6. 6.
    Kumar, D., & Kumar, A. (2013). Design issues for search engines and web crawlers: a review. IOSR Journal of Computer Engineering (IOSR-JCE), 15(6), 34–37.Google Scholar
  7. 7.
    Sharma, S. (2008). Information retrieval in domain specific search engine with machine learning approaches. International Journal of Social, Behavioral, Educational, Economic, Business and Industrial Engineering, 2(6).Google Scholar
  8. 8.
    Meng, W.: Metasearch engines. In Encyclopedia of database systems (pp. 1730–1734).Google Scholar
  9. 9.
    Nwosu, O., & Anyira Echezonam, I. (2011). Inquiry into the use of Google and Yahoo search engines in retrieving web resources by internet users in Nigeria. Indian Journal of Information Sources & Services (IJISS), 1(2), 61–67.Google Scholar
  10. 10.
    Vijaya, P., Raju, G., & Kumar Ray, S. (2014). S-MSE: Asemantic meta search engine using semantic similarity and reputation measure. Journal of Theoretical & Applied Information Technology, 60(2).Google Scholar
  11. 11.
    Ray, S. K., Vijaya, P., & Raju, G. (2013). An ontology based meta-search engine for effective web page retrieval. International Review of Computers and Software (IRECOS), 8(2), 533–541.Google Scholar
  12. 12.
    Poulter, A. (1997). The design of world wide web search engines: A critical review. Program, 31(2).Google Scholar
  13. 13.
    Weideman, M., & Kritzinger, W. (2003). Search engine information retrieval: Empirical research on the usage of meta-tags to enhance website visibility and ranking of e-commerce websites. In Proceedings of the 7th World Conference on Systemics, Cybernetics and Informatics (Vol. 6, pp. 231–236).Google Scholar
  14. 14.
    Vijaya, P., Raju, G., & Ray, S. K. (2016). Artificial neural network-based merging score for Meta search engine. Journal of Central South University, 23(10), 2604–2615.CrossRefGoogle Scholar
  15. 15.
    Naval, P., & Priyanka, S. (2012). A survey on personalized meta search engine. International Journal, 2(3).Google Scholar
  16. 16.
    Jadidoleslamy, H. (2012). Search result merging and ranking strategies in meta-search engines: A survey. IJCSI International Journal of Computer Science Issues, 9(3), 239–251.Google Scholar
  17. 17.
    Maloth, B. (2010). Evaluation of integration algorithms for meta-search engine. Technical report. http://www.bvicam.ac.in/news/INDIACom%202010%20Proceedings/papers/Group3/INDIACom10_261_Paper%20(3).pdf.
  18. 18.
    Jadidoleslamy, H. (2011). Introduction to metasearch engines and result merging strategies: A survey. International Journal of Advances in Engineering & Technology, 1(5), 30–40.Google Scholar
  19. 19.
    Ngu, A. H. H. (2005). Business & economics, In 6th International Conference on Web Information Systems Engineering, WISE 2005, New York, NY, USA.Google Scholar
  20. 20.
    Ngu, A. H. H., Kitsuregawa, M., Neuhold, E., Chung, J.-Y., & Sheng, Q. Z. (2005). Computers. In 6th International Conference on Web Information Systems Engineering, WISE 2005, New York, NY, USA.Google Scholar
  21. 21.
    Li, Z., Wang, Y., & Oria, V. (2001). A new architecture for web meta-search engines. In AMCIS 2001 Proceedings, 31(84), 415–422.Google Scholar
  22. 22.
    Das, S., & Raghuwanshi, K. S. (2014). Search engine selection approach in meta search using past queries. Oriental Journal of Computer Science & Technology, 7(1), 177–183.Google Scholar
  23. 23.
    Rohini, M. (2015). Reformation of query based approach in search engine. International Research Journal of Engineering and Technology (IRJET), 2(5).Google Scholar
  24. 24.
    Fathalla, S. M., Hassan, Y. F., & El-Sayed, M. (2012). A hybrid method for user query reformation and classification. In Proceedings of Computer Theory and Applications (ICCTA), 22nd International Conference on IEEE2012 (pp. 132–138).Google Scholar
  25. 25.
    Pablos, O. (2012). Patricia: Advancing information management through semantic web concepts and ontologies. IGI Global.Google Scholar
  26. 26.
    Leake, D. B., & Scherle, R. (2001). Towards context-based search engine selection. In Proceedings of the 6th International Conference on Intelligent User Interfaces, ACM (pp. 109–112).Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Waljat College of Applied SciencesRusaylSultanate of Oman

Personalised recommendations