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Improving similarity measures of relatedness proximity: Toward augmented concept maps

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  • Sasson, Elan
  • Ravid, Gilad
  • Pliskin, Nava

Abstract

Decision makers relying on web search engines in concept mapping for decision support are confronted with limitations inherent in similarity measures of relatedness proximity between concept pairs. To cope with this challenge, this paper presents research model for augmenting concept maps on the basis of a novel method of co-word analysis that utilizes webometrics web counts for improving similarity measures. Technology assessment serves as a use case to demonstrate and validate our approach for a spectrum of information technologies. Results show that the yielded technology assessments are highly correlated with subjective expert assessments (n=136; r>0.879), suggesting that it is safe to generalize the research model to other applications. The contribution of this work is emphasized by the current growing attention to big data.

Suggested Citation

  • Sasson, Elan & Ravid, Gilad & Pliskin, Nava, 2015. "Improving similarity measures of relatedness proximity: Toward augmented concept maps," Journal of Informetrics, Elsevier, vol. 9(3), pages 618-628.
    • Handle: RePEc:eee:infome:v:9:y:2015:i:3:p:618-628
    DOI: 10.1016/j.joi.2015.06.003
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    References listed on IDEAS

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    Cited by:

    1. Jia Feng & Yun Qiu Zhang & Hao Zhang, 2017. "Improving the co-word analysis method based on semantic distance," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1521-1531, June.
    2. Xiang Zhu & Yunqiu Zhang, 2020. "Co-word analysis method based on meta-path of subject knowledge network," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(2), pages 753-766, May.

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