Study of Thematic and Terminological Transformation of Collections of Scientific Articles in Interdisciplinary Fields of Knowledge

This paper presents the methodology of the quantitative study of the dynamics of changes in the thematic structure of collections and vocabulary of terms that were aimed at selecting documents into thematic collections, creating thematic (classification and subject) profiles of these collections in different chronological periods and a comparative analysis of these profiles. The profiles are treated as descriptive sets, with TF-IDF used as the keyword weight. The methodology is tested in two collections, “Robotics and Robotic Systems” and “Intelligent Systems, Artificial Intelligence, and Machine Learning.”

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Publication Details

Journal
Automatic Documentation and Mathematical Linguistics
Published
2024-12-01
DOI
https://doi.org/10.3103/s0005105524700390
Citations
1
Primary Topic
Library Science and Information
Type
article
Field-Weighted Citation Impact
0.78
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article

Study of Thematic and Terminological Transformation of Collections of Scientific Articles in Interdisciplinary Fields of Knowledge

Н. С. Солошенко, O. V. Fedorets
1 citations
Automatic Documentation and Mathematical Linguistics
Library Science and Information
0.78
article

Study of Thematic and Terminological Transformation of Collections of Scientific Articles in Interdisciplinary Fields of Knowledge

Н. С. Солошенко, O. V. Fedorets
article en
1 citations

Abstract

This paper presents the methodology of the quantitative study of the dynamics of changes in the thematic structure of collections and vocabulary of terms that were aimed at selecting documents into thematic collections, creating thematic (classification and subject) profiles of these collections in different chronological periods and a comparative analysis of these profiles. The profiles are treated as descriptive sets, with TF-IDF used as the keyword weight. The methodology is tested in two collections, “Robotics and Robotic Systems” and “Intelligent Systems, Artificial Intelligence, and Machine Learning.”

Automatic Documentation and Mathematical LinguisticsVol. 58(6)
Russian Academy of Sciences (RU), All Russian Institute for Scientific and Technical Information (RU)
Openalex Percentile: Top 17%
Library Science and Information
0.78
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