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Needs of Scientometry and Possibilities of Modern Machine Learning as a Field of Artificial Intelligence

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Scientific and Technical Information Processing Aims and scope

Abstract—

A general description of modern scientometry, its main tasks, and its research methods is presented. The issues of the application of conventional machine learning and deep learning algorithms as tools of artificial intelligence in the thematic classification of scientific literature are considered. The problems and limitations of the classification of literature by sections of science in the systems of indexing and citing of scientific information are outlined. The author presents a specific example of a deep learning application for by-article thematic classification based on convolutional neural networks that was designed by scientists from the United Arab Emirates and Jordan. The article emphasizes the importance of the use of deep learning applications and models for creating correct classifications of the scientific literature that correspond to the realities of the development of science and that are capable of increasing the accuracy of calculating scientometric indicators.

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Funding

The article was supported by the state assignment of the All-Russian Research Institute for Scientific and Technical Information, Russian Academy of Sciences (project no. FFFU-2022-0007).

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  1. All-Russian Research Institute for Scientific and Technical Information, Russian Academy of Sciences, Moscow, Russia

    E. V. Melnikova

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  1. E. V. Melnikova
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Correspondence to E. V. Melnikova.

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Melnikova, E.V. Needs of Scientometry and Possibilities of Modern Machine Learning as a Field of Artificial Intelligence. Sci. Tech. Inf. Proc. 50, 114–120 (2023). https://doi.org/10.3103/S0147688223020089

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