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A pragmatic approach to hierarchical categorization of research expertise in the presence of scarce information

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Abstract

Throughout the history of science, different knowledge areas have collaborated to overcome major research challenges. The task of associating a researcher with such areas makes a series of tasks feasible such as the organization of digital repositories, expertise recommendation and the formation of research groups for complex problems. In this article, we propose a simple yet effective automatic classification model that is capable of categorizing research expertise according to a knowledge area classification scheme. Our proposal relies on discriminatory evidence provided by the title of academic works, which is the minimum information capable of relating a researcher to its knowledge area. Our experiments show that using supervised machine learning methods trained with manually labeled information, it is possible to produce effective classification models.

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Notes

  1. http://dl.acm.org.

  2. http://dblp.uni-trier.de.

  3. https://www.ncbi.nlm.nih.gov/pubmed/.

  4. http://www.ndltd.org.

  5. http://lattes.cnpq.br.

  6. http://www.cnpq.br/documents/10157/186158/TabeladeAreasdoConhecimento.pdf.

  7. In this article we use the terms classification and categorization interchangeably.

  8. Available at: http://www.lbd.dcc.ufmg.br/lbd/collections/hierarchical-categorization-of-research-expertise.

  9. http://scikit-learn.org.

  10. Literal translation from the original title in Portuguese:  A UNESCO e o Mundo da Cultura.

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Acknowledgements

This work was partially funded by project MASWeb (Grant FAPEMIG/PRONEX APQ-01400-14) and by the authors’ individual Grants from CAPES, CNPq and FAPEMIG.

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Authors and Affiliations

  1. Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil

    Gustavo Oliveira de Siqueira, Sérgio Canuto, Marcos André Gonçalves & Alberto H. F. Laender

Authors
  1. Gustavo Oliveira de Siqueira
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  2. Sérgio Canuto
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  3. Marcos André Gonçalves
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  4. Alberto H. F. Laender
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Corresponding authors

Correspondence to Gustavo Oliveira de Siqueira or Alberto H. F. Laender.

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de Siqueira, G.O., Canuto, S., Gonçalves, M.A. et al. A pragmatic approach to hierarchical categorization of research expertise in the presence of scarce information. Int J Digit Libr 21, 61–73 (2020). https://doi.org/10.1007/s00799-018-0260-z

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  • DOI: https://doi.org/10.1007/s00799-018-0260-z

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