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Using a compound approach based on elaborated neural network for Webometrics: An example issued from the EICSTES project

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Listed:
  • Jean-Charles Lamirel

    (LORIA)

  • Shadi Al Shehabi

    (LORIA)

  • Claire Francois

    (URI/INIST-CNRS)

  • Xavier Polanco

    (URI/INIST-CNRS)

Abstract

This paper present a compound approach for Webometrics based on an extension the self-organizing multimap MultiSOM model. The goal of this new approach is to combine link and domain clustering in order to increase the reliability and the precision of Webometrics studies. The extension proposed for the MultiSOM model is based on a Bayesian network-oriented approach. A first experiment shows that the behaviour of such an extension is coherent with its expected properties for Webometrics. A second experiment is carried out on a representative Web dataset issued from the EISCTES IST project context. In this latter experiment each map represents a particular viewpoint extracted from the Web data description. The obtained maps represented either thematic or link classifications. The experiment shows empirically that the communication between these classifications provides Webometrics with new explaining capabilities.

Suggested Citation

  • Jean-Charles Lamirel & Shadi Al Shehabi & Claire Francois & Xavier Polanco, 2004. "Using a compound approach based on elaborated neural network for Webometrics: An example issued from the EICSTES project," Scientometrics, Springer;Akadémiai Kiadó, vol. 61(3), pages 427-441, November.
    • Handle: RePEc:spr:scient:v:61:y:2004:i:3:d:10.1023_b:scie.0000045119.88828.ce
    DOI: 10.1023/B:SCIE.0000045119.88828.ce
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    References listed on IDEAS

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    1. Alexander Kopcsa & Edgar Schiebel, 1998. "Science and technology mapping: A new iteration model for representing multidimensional relationships," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 49(1), pages 7-17.
    2. Xia Lin, 1997. "Map displays for information retrieval," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 48(1), pages 40-54, January.
    3. S. E. Robertson & K. Sparck Jones, 1976. "Relevance weighting of search terms," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 27(3), pages 129-146, May.
    4. Jean-Charles Lamirel & Claire Francois & Shadi Al Shehabi & Martial Hoffmann, 2004. "New classification quality estimators for analysis of documentary information: Application to patent analysis and web mapping," Scientometrics, Springer;Akadémiai Kiadó, vol. 60(3), pages 445-562, August.
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