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.
Similar content being viewed by others
References
IST-1999-20350.
L. M. De Campos, J. M. FernÁndez-Luna, J. F. Huete, A layered Bayesian network model for document retrieval. ECIR, (2000) 169–182.
L. M. De Campos, J. M. FernÁndez-Luna, J. F. Huete, Building Bayesian network-based information retrieval systems. DEXA Workshop 2000, pp. 543–552.
P. Ingwersen, Webometrics: basic concepts, ISSI 2003, Beijing, China, August 2003.
T. Kohonen, Self-Organisation and Associative Memory, 3rd edition, Springer Verlag, Berlin, 1984.
T. Kohonen, The self-organizing map, Proceedings of the IEEE, 78 (1990) 1464–1480.
T. Kohonen, Self-Organizing Maps. Springer Verlag, Berlin, 1997.
A. Kopcsa, E. Schiebel, Science and technology mapping: A new iteration model for representing multidimensional relationships, JASIS, 49 (1998) 7–17.
J. C. Lamirel, Y. Toussaint, C. Francois, X. Polanco, Using artificial neural networks for mapping of science and technology: application to patents analysis, Proceedings of ISSI 2001, Sydney, Australia, July 2001.
J. C. Lamirel, C. Francois, S. Al Shehabi, M. Hoffmann, New classification quality estimators for analysis of documentary information: application to patent analysis and web mapping, Scientometrics, 60 (3) (2004) 445–462.
J. C. Lamirel, C. Francois, S. Al Shehabi, M. Hoffmann, Intelligent patent analysis through the use of a neural network: experiment of multi-viewpoint analysis with the MultiSOM model, Proceedings of ACL 2003, Sapporo, Japan, July 2003.
J. C. Lamirel, Application d'une approche symbolico-connexionniste pour la conception d'un système documentaire hautement interactif, Thèse de l'Université de Nancy 1 Henri Poincaré, 1995.
X. Lin, D. Soergel, G. Marchionini, A self-organizing semantic map for information retrieval, In: Proceedings of the 4th International SIGIR Conference on R & D in Information Retrieval, 13-16 October, Chicago, 1991, pp. 262–269.
X. Lin, Map displays for information retrieval, JASIS, 48 (1997) 40–54.
B. A. Ribeiro-Neto, R. R. Muntz, A belief network model for IR. In: Proceedings of the 19 ACM-SIGIR Conference on Research and Development in Information Retrieval, (1996) pp. 253–260
G. Salton, The SMART Retrieval System: Experiments in Automatic Document Processing, Prentice Hall Inc., Englewood Cliffs, New Jersey, 1971.
S. E. Roberston, K. Spark Jones, Relevance weighting of search terms, Journal of the American Society for Information Science, 27 (1976) 129–146.
SOM Papers, http://www.cis.hut.fi/nnrc/refs/
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Lamirel, JC., Shehabi, S.A., Francois, C. et al. Using a compound approach based on elaborated neural network for Webometrics: An example issued from the EICSTES project. Scientometrics 61, 427–441 (2004). https://doi.org/10.1023/B:SCIE.0000045119.88828.ce
Issue Date:
DOI: https://doi.org/10.1023/B:SCIE.0000045119.88828.ce