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Extending Knowledge-Based Profile Matching in the Human Resources Domain

Part of the Lecture Notes in Computer Science book series (LNISA,volume 9262)

Abstract

In the Human Resources domain the accurate matching between job positions and job applicants profiles is crucial for job seekers and recruiters. The use of recruitment taxonomies has proven to be of significant advantage in the area by enabling semantic matching and reasoning. Hence, the development of Knowledge Bases (KB) where curricula vitae and job offers can be uploaded and queried in order to obtain the best matches by both, applicants and recruiters is highly important. We introduce an approach to improve matching of profiles, starting by expressing jobs and applicants profiles by filters representing skills and competencies. Filters are used to calculate the similarity between concepts in the subsumption hierarchy of a KB. This is enhanced by adding weights and aggregates on filters. Moreover, we present an approach to evaluate over-qualification and introduce blow-up operators that transform certain role relations such that matching of filters can be applied.

Keywords

  • HR Domain
  • Human Resources
  • Application Profile
  • Matching Measurement
  • Lattice-like Structure

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The research reported in this paper was supported by the Austrian Forschungsförderungsgesellschaft (FFG) for the Bridge project “Accurate and Efficient Profile Matching in Knowledge Bases” (ACEPROM) under contract 841284.

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Correspondence to Alejandra Lorena Paoletti .

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Paoletti, A.L., Martinez-Gil, J., Schewe, KD. (2015). Extending Knowledge-Based Profile Matching in the Human Resources Domain. In: Chen, Q., Hameurlain, A., Toumani, F., Wagner, R., Decker, H. (eds) Database and Expert Systems Applications. Globe DEXA 2015 2015. Lecture Notes in Computer Science(), vol 9262. Springer, Cham. https://doi.org/10.1007/978-3-319-22852-5_3

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  • DOI: https://doi.org/10.1007/978-3-319-22852-5_3

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