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WEB Information Retrieval Models

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Encyclopedia of Database Systems

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Synonyms

Web search engines

Definition

The Web can be considered as a large-scale document collection, for which classical text retrieval techniques can be applied. However, its unique features and structure offer new sources of evidence that can be used to enhance the effectiveness of Information Retrieval (IR) systems. Generally, Web IR examines the combination of evidence from both the textual content of documents and the structure of the Web, as well as the search behavior of users and issues related to the evaluation of retrieval effectiveness in the Web setting.

Web Information Retrieval models are ways of integrating many sources of evidence about documents, such as the links, the structure of the document, the actual content of the document, the quality of the document, etc. so that an effective Web search engine can be achieved. In contrast with the traditional library-type settings of IR systems, the Web is a hostile environment, where Web search engines have to deal with...

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Recommended Reading

  1. Brin S. and Page L. The anatomy of a large-scale hypertextual Web search engine. Comput. Netw. ISDN Syst., 30(1–7):107–117, 1998.

    Article  Google Scholar 

  2. Craswell N., Hawking D., and Robertson S. Effective site finding using link anchor information. In Proc. 24th Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, 2001, pp. 250–257.

    Google Scholar 

  3. Craswell N., Robertson S., Zaragoza H., and Taylor M. Relevance weighting for query independent evidence. In Proc. 31st Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, 2005, pp. 416–423.

    Google Scholar 

  4. Hawking D. and Craswell N. The very large collection and Web tracks. In TREC: Experiment and Evaluation in Information Retrieval. Kluwer Academic Publishers, Dordrecht, 2004, pp. 199–232.

    Google Scholar 

  5. Joachims T., Li H., Liu T.Y., and Zhai C. SIGIR workshop report: learning to rank for information retrieval (LR4IR 2007). SIGIR Forum, 41(2):55–62, 2007.

    Article  Google Scholar 

  6. Kleinberg J.M. Authoritative sources in a hyperlinked environment. J. ACM, 46(5):604–632, 1999.

    Article  MATH  MathSciNet  Google Scholar 

  7. Kraaij W., Westerveld T., and Hiemstra D. The importance of prior probabilities for entry page search. In Proc. 25th Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, 2002, pp. 27–34.

    Google Scholar 

  8. Macdonald C., Plachouras V., He B., Lioma C., and Ounis I. University of Glasgow at WebCLEF 2005: Experiments in per-field normlisation and language specific stemming. In Proc. 6th Workshop, Cross-Language Evaluation Forum, 2005, pp. 898–907.

    Google Scholar 

  9. Ogilvie P. and Callan J. Combining document representations for known-item search. In Proc. 26th Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, 2003, pp. 143–150.

    Google Scholar 

  10. Peng J., Macdonald C., He B., and Ounis I. Combination of document priors in Web information retrieval. In Proc. 8th Int. Conf. Computer-Assisted Information Retrieval, 2007.

    Google Scholar 

  11. Plachouras V. Selective Web Information Retrieval. PhD thesis, Department of Computing Science, University of Glasgow, 2006.

    Google Scholar 

  12. Plachouras V. and Ounis I. Multinomial randomness models for retrieval with document fields. In Proc. 29th European Conf. on IR Research, 2007, pp. 28–39.

    Google Scholar 

  13. Plachouras V., Ounis I., and Amati G. The static absorbing model for the Web. J. Web Eng., 165–186, 2005.

    Google Scholar 

  14. Robertson S., Zaragoza H., and Taylor M. Simple BM25 extension to multiple weighted fields. In Proc. Int. Conf. on Information and Knowledge Management, 2004, pp. 42–49.

    Google Scholar 

  15. Silverstein C., Henzinger M., Marais H., and Moricz M. Analysis of a very large AltaVista Query Log. Technical Report 1998-014, Digital SRC, 1998.

    Google Scholar 

  16. Zaragoza H., Craswell N., Taylor M., Saria S., and Robertson S. Microsoft cambridge at TREC-13: Web and HARD tracks. In Proc. the 4th Text Retrieval Conf., 2004.

    Google Scholar 

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Author information

Authors and Affiliations

  1. University of Glasgow, Glasgow, UK

    Craig MacDonald & Iadh Ounis

Authors
  1. Craig MacDonald
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  2. Iadh Ounis
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Editor information

Editors and Affiliations

  1. College of Computing, Georgia Institute of Technology, 266 Ferst Drive, 30332-0765, Atlanta, GA, USA

    LING LIU (Professor) (Professor)

  2. Database Research Group David R. Cheriton School of Computer Science, University of Waterloo, 200 University Avenue West, N2L 3G1, Waterloo, ON, Canada

    M. TAMER ÖZSU (Professor and Director, University Research Chair) (Professor and Director, University Research Chair)

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© 2009 Springer Science+Business Media, LLC

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MacDonald, C., Ounis, I. (2009). WEB Information Retrieval Models. In: LIU, L., ÖZSU, M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-39940-9_928

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