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Automated comparison of scientific research based on academic genealogy

https://doi.org/10.32517/0234-0453-2025-40-6-16-27

Abstract

   The article addresses the problem of analyzing large corpora of scientific texts. It is noted that traditional scientometric approaches based on citation analysis do not always capture the substantive proximity of ideas or trace the intellectual continuity of scientific knowledge. The paper proposes an algorithm that combines the capabilities of large language models (LLMs) with quantitative analytical methods. The approach proceeds in two stages. At the first stage, LLM is used to automatically structure the text of a scientific paper and convert it into a machine-readable format. This yields, for each work, a vector representation of its thematic profile. At the second stage, thematic profiles of individual works and of pre-specified groups (schools of thought) are compared using centroid distances and clustering techniques. The ultimate goal is to move beyond formal relational analyses among researchers (e. g., “supervisor — dissertation candidate”) toward constructing an intellectual genealogy that tracks the evolution of schools of thought on the basis of the substantive proximity of their ideas. It is emphasized that the proposed approach does not replace expert assessment but serves as an auxiliary tool for navigating the scientific literature and identifying connections between studies.

About the Authors

A. Kh. Marinosyan
Moscow City University
Russian Federation

Andreas Kh. Marinosyan, postgraduate student

Institute of Digital Education; Department of Education Informatisation

Moscow



S. G. Grigoriev
Moscow City University
Russian Federation

Sergey G. Grigoriev, Corresponding Member of the RAE, Doctor of Sciences (Engineering), Professor, Professor at the Department

Institute of Digital Education; Department of IT, Management and Technology

Moscow



I. M. Lerner
Kazan National Research Technical University named after A. N. Tupolev-KAI; Kazan (Volga Region) Federal University
Russian Federation

Ilya M. Lerner, Doctor of Sciences (Engineering), Professor, Professor at the
Department

Institute of Radio Electronics, Photonics and Digital Technologies; Department of Nanotechnology in Electronics; Institute of Physics; Department of Radio Astronomy

The Republic of Tatarstan; Kazan



M. A. Anikieva
Siberian Federal University
Russian Federation

Marina A. Anikieva, Candidate of Sciences (Engineering), Associate Professor

School of Space and Information Technology; Department of Artificial Intelligence Systems

Krasnoyarsk



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Review

For citations:


Marinosyan A.Kh., Grigoriev S.G., Lerner I.M., Anikieva M.A. Automated comparison of scientific research based on academic genealogy. Informatics and education. 2025;40(6):16-27. (In Russ.) https://doi.org/10.32517/0234-0453-2025-40-6-16-27

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ISSN 0234-0453 (Print)
ISSN 2658-7769 (Online)