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Algoritmos de clasificación de documentos científicos: pasado y presente

  • Álvarez-Llorente, Jesús M. [1] ; Guerrero-Bote, Vicente P. [1] Árbol académico ; De-Moya-Anegón, Félix [2] Árbol académico
    1. [1] Universidad de Extremadura

      Universidad de Extremadura

      Badajoz, España

    2. [2] SCImago Research Group
  • Localización: Infonomy, ISSN-e 2990-2290, Vol. 3, Nº. 4, 2025 (Ejemplar dedicado a: Algoritmos)
  • Idioma: español
  • DOI: 10.3145/infonomy.25.026
  • Títulos paralelos:
    • Algorithms for Scientific Documents: Past and Present
  • Enlaces
  • Resumen
    • español

      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.

    • English

      This study offers a comprehensive overview of document-level classification algorithms in scientific research, proposed as an alternative to the journal-based categorizations employed by major bibliographic databases such as Web of Science and Scopus. These journal-driven schemes often introduce significant inaccuracies in both information retrieval and research evaluation, as they fail to categorize articles in accordance with their actual content.

      First, we provide a historical review of the main approaches developed since the emergence of scientific databases, highlighting their contributions as well as their limitations. Automatic clustering techniques and community detection algorithms have represented important advances in the organization of scientific knowledge, yet they cannot 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, starting from existing journal categorizations, reclassify articles into the same thematic hierarchies used by bibliographic databases, relying primarily on the analysis of straightforward citation and reference patterns.

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