V.N. Gureev
N.A. Mazov

: Scientific and Technical Information Processing

This paper considers a new model for forming ranked lists of scientific journals on the basis of a bibliometric analysis. The model is based on searching in an abstract database of a set of articles that is semantically equivalent to a set of articles of a user or group of users, for whom a repertoire of scientific periodicals is selected. In other words, a query must result in an assembly of articles with the same thematic orientation, which is expressed in the articles of an author. To achieve this goal, KeyWords Plus were used as a query for articles by workers from three scientific organizations in different scientific fields (biomedical, geological, and physico-mathematical fields). KeyWords Plus, which give a short retelling of each article, are joined in a query into groups, whose number was correspondingly equal to the number of articles of workers from these organizations. The query yielded lists of articles with the same sequences of keywords, which proved their semantic proximity. Using different filters we analyzed the groups of journals that focused articles that are of interest to us to the greatest extent. The obtained lists were compared with the lists of journals that are most cited by the authors of the articles; the similarity of the lists in the field of natural sciences was ascertained to be significant and the similarity in the field of exact sciences was shown to be less. The possible spheres for the application of the KeyWords Plus method, which is an alternative to an analysis of citations, as well as the applicability of a new model to the Scopus database, where controlled thesauri are used as additional keywords, are described.