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

Jean-Charles Lamirel (), Shadi Al Shehabi, Claire Francois and Xavier Polanco
Additional contact information
Jean-Charles Lamirel: LORIA
Shadi Al Shehabi: LORIA
Claire Francois: URI/INIST-CNRS
Xavier Polanco: URI/INIST-CNRS

Scientometrics, 2004, vol. 61, issue 3, No 9, 427-441

Abstract: 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.

Date: 2004
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DOI: 10.1023/B:SCIE.0000045119.88828.ce

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