Algoritmos para documentos científicos: pasado y presente

Álvarez-Llorente, Jesús M., Guerrero-Bote, Vicente P. and De-Moya-Anegón, Félix Algoritmos para documentos científicos: pasado y presente. Infonomy, 2025, vol. 3, n. 4. [Journal article (Unpaginated)]

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English abstract

This study offers a comprehensive overview of document-level classification algo-rithms in scientific research, proposed as an alternative to the journal-based categori-zations employed by major bibliographic databases such as Web of Science and Sco-pus. These journal-driven schemes often introduce significant inaccuracies in both in-formation retrieval and research evaluation, as they fail to categorize articles in accord-ance with their actual content. First, we provide a historical review of the main approaches developed since the emer-gence of scientific databases, highlighting their contributions as well as their limita-tions. Automatic clustering techniques and community detection algorithms have rep-resented important advances in the organization of scientific knowledge, yet they can-not serve as a practical substitute for journal-based classifications. Other approaches, such as those relying on neural networks or text mining, face scalability issues that prevent their application at the global level of science. The most recent and promising strategies are built upon simple algorithms that, start-ing from existing journal categorizations, reclassify articles into the same thematic hi-erarchies used by bibliographic databases, relying primarily on the analysis of straight-forward citation and reference patterns.

Spanish abstract

Este trabajo se presenta como una recopilación de algoritmos de clasificación de la investigación a nivel de artículo como alternativa a las clasificaciones por revistas que se emplean en las grandes bases de datos de ciencia como Web of Science o Scopus, las cuales causan gran imprecisión en las búsquedas y en la evaluación de la ciencia, ya que utilizando éstas, los artículos no resultan categorizados con fidelidad respecto a su verdadero contenido. En primer lugar hacemos una revisión histórica de las principales ideas planteadas a lo largo de los años desde la misma aparición de las bases de datos, detectando sus contribuciones y sus limitaciones. Los algoritmos de agrupamiento automático y de detección de comunidades han supuesto grandes avances en organización de la ciencia, pero no resultan aplicables como alternativa a la clasificación por revistas. Otros algoritmos no son escalables al conjunto de la ciencia debido a su complejidad, como los basados en redes neuronales o minería de textos. Las propuestas más recientes y prometedoras responden a algoritmos sencillos que, partiendo de la categorización por revistas, reclasifican los artículos en las mismas jerarquías temáticas de las bases de datos, mediante el análisis de simples citas y referencias.

Item type: Journal article (Unpaginated)
Keywords: Algoritmos de clasificación; Clasificaciones a nivel de documento; Clasificaciones; Clasificación de la ciencia; Bases de datos de ciencia; Cienciometría; Citación; Esquemas de clasificación; ASJC; Scopus; Web of Science; Classification algorithms; Document-level classifications; Classifications; Science classification; Scientific databases; Scientometrics; Citation; Classification schemes; ASJC; Scopus; Web of Science.
Subjects: H. Information sources, supports, channels. > HN. e-journals.
H. Information sources, supports, channels. > HP. e-resources.
I. Information treatment for information services > IC. Index languages, processes and schemes.
I. Information treatment for information services > IE. Data and metadata structures.
L. Information technology and library technology > LJ. Software.
Depositing user: Tomàs Baiget
Date deposited: 14 Sep 2025 15:16
Last modified: 14 Sep 2025 16:36
URI: http://hdl.handle.net/10760/47133

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