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

Jean-Charles Lamirel 1 Shadi Al Shehabi 1 Claire François Xavier Polanco
1 CORTEX - Neuromimetic intelligence
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
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.
keyword : typology neural method multiview bayesian network hyperlink multivue réseau bayésien webométrie lien hypertexte typologie méthode neuronale
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Article dans une revue
Scientometrics, Springer Verlag, 2004, 61 (3), pp.427-441
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https://hal.inria.fr/inria-00100154
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Soumis le : mardi 26 septembre 2006 - 10:14:48
Dernière modification le : jeudi 11 janvier 2018 - 06:19:48

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Jean-Charles Lamirel, Shadi Al Shehabi, Claire François, Xavier Polanco. Using a compound approach based on elaborated neural network for Webometrics: An example issued from the EICSTES project. Scientometrics, Springer Verlag, 2004, 61 (3), pp.427-441. 〈inria-00100154〉

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