The Use of Thematic Analysis Methods in Scientometric Systems

Abstract

Modern scientometric systems and citation systems use various mechanisms of thematic search and thematic filtering of information. In most cases, a full-text approach is used for thematic analysis of articles and journals, which has a number of limitations. The use of algorithms based on graph analysis, both independently and in conjunction with full-text algorithms, eliminates these limitations and improves the completeness and accuracy of subject search. The algorithm developed by the authors and presented in this work uses the co-authorship graph to analyze the thematic proximity of journals. The algorithm is insensitive to the language of the journal and selects similar journals in different languages, which is difficult to implement for algorithms based on the analysis of full-text information. The algorithm was tested in the scientometric system IAS ISTINA. In the interface developed for these purposes, the user can select one journal that is close to him on the subject, and the system will automatically generate a selection of journals that may be of interest to the user both in terms of studying the materials available in them and in terms of publishing his own articles. In the future, the developed algorithm can be adapted to search for similar conferences, collections of publications and scientific projects. The presence of such a tool will increase the publication activity of young employees, increase the citation rate of articles and the citation rate between journals. The results of the algorithm for determining thematic proximity between journals, collections, conferences and scientific projects can also be used to build rules in models of differentiating access to data based on domain ontologies.