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Automatic Hierarchical Categorization of Research Expertise Using Minimum Information

  • Gustavo Oliveira de Siqueira
  • Sérgio Canuto
  • Marcos André Gonçalves
  • Alberto H. F. LaenderEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10450)

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 paper we propose a simple yet effective automatic classification model that is capable of categorizing research expertise according to a hierarchical knowledge area classification scheme. Our proposal relies on discriminative evidence provided by the title of academic works, which is the minimum information capable of relating a researcher to its knowledge area. We also evaluate the use of learning-to-rank as an effective mean to rank experts with minimum information. Our experiments show that using supervised machine learning methods trained with manually labeled information, it is possible to produce effective classification and ranking models.

Keywords

Research expertise categorization Classification schemes Supervised classification Learning-to-rank 

Notes

Acknowledgements

This work was partially funded by projects InWeb (grant MCT/CNPq 573871/2008-6) and MASWeb (grant FAPEMIG/PRONEX APQ-01400-14), and by the authors’ individual grants from CAPES, CNPq and FAPEMIG.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Gustavo Oliveira de Siqueira
    • 1
  • Sérgio Canuto
    • 1
  • Marcos André Gonçalves
    • 1
  • Alberto H. F. Laender
    • 1
    Email author
  1. 1.Department of Computer ScienceUniversidade Federal de Minas GeraisBelo HorizonteBrazil

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