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Professional Competence Identification Through Formal Concept Analysis

  • Paula R. Silva
  • Sérgio M. Dias
  • Wladmir C. Brandão
  • Mark A. Song
  • Luis E. Zárate
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 321)

Abstract

As the job market has become increasingly competitive, people who are looking for a job placement have needed help to increase their competence to achieve a job position. The competence is defined by the set of skills that is necessary to execute an organizational function. In this case, it would be helpful to identify the sets of skills which is necessary to reach job positions. Currently, the on-line professional social networks are attracting the interest from people all around the world, whose their goals are oriented to business relationships. Through the available amount of information in this kind of networks it is possible to apply techniques to identify the competencies that people have developed in their career. In this scenario it has been fundamental the adoption of computational methods to solve this problem. The formal concept analysis (FCA) has been a effective technique for data analysis area, because it allows to identify conceptual structures in data sets, through conceptual lattice and implications. A specific set of implications, know as proper implications, represent the set of conditions to reach a specific goal. So, in this work, we proposed a FCA-based approach to identify and analyze the professional competence through proper implications.

Keywords

Formal concept analysis Proper implications Professional competence On-line social networks 

Notes

Acknowledgement

The authors acknowledge the financial support received from the Foundation for Research Support of Minas Gerais state, FAPEMIG; the National Council for Scientific and Technological Development, CNPq; Coordination for the Improvement of Higher Education Personnel, CAPES. We would also express gratitude to the Federal Service of Data Processing, SERPRO.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Pontifical Catholic University of Minas Gerais (PUC Minas)Belo HorizonteBrazil
  2. 2.Federal Service of Data Processing (SERPRO)Belo HorizonteBrazil

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