Special Issue on Machine Learning in Scientometrics

Journal of Scientometric Research,2019,8,2s,s1.
Published:October 2019
Type:Editor’s Note
Authors:
Author(s) affiliations:

Snehanshu Saha, PES University and Center for AstroInformatics , Bangalore, Karnataka, INDIA.

Saibal Kar, Centre for Studies in Social Sciences, Calcutta and University of Bonn, GERMANY.

Abstract:

Scientometrics is a domain that performs a quantitative and qualitative assessment of research and scientific progress. The field has earned popularity in last few years owing to the need to measure research outputs at individual, institutional and geographical levels. As a result of this need, different parameters are brought-up and various databases like Scopus, Web of Science and Google scholar are built for computation of these parameters. The data generated and stored as a result of proliferation of research papers and other scientific activities is vast. Analysis of the data cannot be performed without the intervention of sophisticated tools and techniques. Consequently, the use of Machine leaning algorithms for carrying out tasks like classification, regression, clustering and associations on these databases becomes imminent. The indicators to mark research performance use citation information in a well-defined way. Citations have become a key component in evaluating performance for authors, articles and journals. To evaluate the role of Machine Learning in Scientometrics, ML techniques can help in predicting citation count, can provide useful insights on computing new bibliometric indexes and also, in finding associations among them. The usage of powerful statistical tools like multiple linear regression, convex/ concave optimization and gradient ascent/descent algorithms can be explored in scientometric and bibliographic analysis. Read more...

Keywords:NiL

Special Issue on Machine Learning in Scientometrics

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Saha S, Kar S. Special Issue on Machine Learning in Scientometrics. Journal of Scientometric Research. 2019;8(2s):s1.