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Web-Page Indexing Based on the Prioritize Ontology Terms

  • Debajyoti MukhopadhyayEmail author
  • Sukanta Sinha
Chapter
Part of the Cognitive Intelligence and Robotics book series (CIR)

Abstract

In recent years, globalization has become one of the most basic and popular human trends. To globalize information, people always publish their documents in the Internet. As a result, the volume of information in Internet becomes huge and it is still growing at an alarming rate. To handle such huge volume of information, Web-searcher uses search engines. However, one of the most practical issues in this area is to design new efficient search engine that retrieves specific information from those pool of information. Therefore, such kind of problem has got an important attention in today’s human life. However, several web researchers are involved to design efficient search engine by optimizing their algorithms, identifying important parameters, etc. Among them, Web-page indexing has been identified as a crucial parameter. Nevertheless, there are several approaches that have been proposed to index Web-pages by the Web researchers.

References

  1. 1.
    T. Berners-Lee, M. Fischetti, Weaving the Web: The Original Design and Ultimate Destiny of the World Wide Web by Its Inventor (Harper Business, New York, 1999)Google Scholar
  2. 2.
    B.M. Leiner, V.G. Cerf, D.D. Clark, R.E. Kahn, L. Kleinrock, D.C. Lynch, J. Postel, L.G. Roberts, S. Wolff, A brief history of internet. ACM Comput. Commun. 35(1), 22–31 (2009).  https://doi.org/10.1145/1629607.1629613CrossRefGoogle Scholar
  3. 3.
    W. Willinger, R. Govindan, S. Jamin, V. Paxson, S. Shenker, Scaling phenomena in the internet, in Proceedings of the National Academy of Sciences, New York (2002), pp. 2573-2580Google Scholar
  4. 4.
    J.J. Rehmeyer, Mapping a Medusa: the internet spreads its tentacles. Sci. News 171(25), 387–388 (2007).  https://doi.org/10.1002/scin.2007.5591712503CrossRefGoogle Scholar
  5. 5.
    M.E. Bates, D. Anderson, Free, Fee-Based and Value-Added Information Services Factiva. The Factiva 2002 White Paper Series (Dow-Jones Reuters Business Interactive, LLC, 2002)Google Scholar
  6. 6.
    D. Hawking, N. Craswell, P. Bailey, K. Griffihs, Measuring search engine quality. Inf. Retrieval 4(1), 33–59 (2001)CrossRefGoogle Scholar
  7. 7.
    T. Joachims, Optimizing search engines using clickthrough data, in Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’02), Edmonton, Alberta, Canada (2002), pp. 133–142Google Scholar
  8. 8.
    D. Mukhopadhyay, S.R. Singh, Two novel methodologies for searching the web: confidence based and hyperlink-content based. Haldia Institute of Technology, Department of Computer Science & Engineering Research Report (2003)Google Scholar
  9. 9.
    R. Baeza-Yates, C. Hurtado, M. Mendoza, G. Dupret, Modeling user search behavior, in Proceedings of the Third Latin American Web Congress (LA-WEB’2005), Buenos Aires, Argentina (2005), pp. 242–251Google Scholar
  10. 10.
    O. Hoeber, Web information retrieval support systems: the future of web search, in IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT ’08), IEEE Computer Society (2008), pp. 29–32Google Scholar
  11. 11.
    T.P.C. Silva, E.S. de Moura, J.M.B. Cavalcanti, A.S. da Silva, M.G. de Carvalho, M.A. Gonc-alves, An evolutionary approach for combining different sources of evidence in search engines. Inf. Syst. 34, 276–289 (2009)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Web Intelligence and Distributed Computing Research Lab, Computer Engineering DepartmentNHITM of Murnbai UniversityKavesar, Thane (W)India
  2. 2.Wipro LimitedBrisbaneAustralia

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