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Using artificial neural networks for mapping of scienceand technology: A multi-self-organizing-maps approach

Author

Listed:
  • Xavier Polanco

    (URI/INIST-CNRS)

  • Claire François

    (URI/INIST-CNRS)

  • Jean-Charles Lamirel

    (LORIA)

Abstract

We argue in favour of artificial neural networks for exploratory data analysis, clustering andmapping. We propose the Kohonen self-organizing map (SOM) for clustering and mappingaccording to a multi-maps extension. It is consequently called Multi-SOM. Firstly the KohonenSOM algorithm is presented. Then the following improvements are detailed: the way of namingthe clusters, the map division into logical areas, and the map generalization mechanism. Themulti-map display founded on the inter-maps communication mechanism is exposed, and thenotion of the viewpoint is introduced. The interest of Multi-SOM is presented for visualization,exploration or browsing, and moreover for scientific and technical information analysis. A casestudy in patent analysis on transgenic plants illustrates the use of the Multi-SOM. We also showthat the inter-map communication mechanism provides support for watching the plants on whichpatented genetic technology works. It is the first map. The other four related maps provideinformation about the plant parts that are concerned, the target pathology, the transgenictechniques used for making these plants resistant, and finally the firms involved in geneticengineering and patenting. A method of analysis is also proposed in the use of this computerbasedmulti-maps environment. Finally, we discuss some critical remarks about the proposedapproach at its current state. And we conclude about the advantages that it provides for aknowledge-oriented watching analysis on science and technology. In relation with this remark weintroduce in conclusion the notion of knowledge indicators.

Suggested Citation

  • Xavier Polanco & Claire François & Jean-Charles Lamirel, 2001. "Using artificial neural networks for mapping of scienceand technology: A multi-self-organizing-maps approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 51(1), pages 267-292, April.
    • Handle: RePEc:spr:scient:v:51:y:2001:i:1:d:10.1023_a:1010537316758
    DOI: 10.1023/A:1010537316758
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    References listed on IDEAS

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    1. E.C.M. Noyons & H.F. Moed & M. Luwel, 1999. "Combining mapping and citation analysis for evaluative bibliometric purposes: A bibliometric study," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 50(2), pages 115-131.
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    Cited by:

    1. Yu-Shan Chen & Ke-Chiun Chang, 2009. "Using neural network to analyze the influence of the patent performance upon the market value of the US pharmaceutical companies," Scientometrics, Springer;Akadémiai Kiadó, vol. 80(3), pages 637-655, September.
    2. Yu-Shan Chen & Ke-Chiun Chang, 2010. "The nonlinear nature of the relationships between the patent traits and corporate performance," Scientometrics, Springer;Akadémiai Kiadó, vol. 82(1), pages 201-210, January.
    3. Yu-Shan Chen & Ke-Chiun Chang, 2010. "Analyzing the nonlinear effects of firm size, profitability, and employee productivity on patent citations of the US pharmaceutical companies by using artificial neural network," Scientometrics, Springer;Akadémiai Kiadó, vol. 82(1), pages 75-82, January.
    4. Yu-Shan Chen & Yu-Hsien Lin & Tai-Hsi Wu & Shu-Tzu Hung & Pei-Ju Lucy Ting & Chen-Han Hsieh, 2019. "Re-examine the determinants of market value from the perspectives of patent analysis and patent litigation," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(1), pages 1-17, July.
    5. 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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