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East European Conference on Advances in Databases and Information Systems

ADBIS 2016: New Trends in Databases and Information Systems pp 157–167Cite as

A Smart Approach for Matching, Learning and Querying Information from the Human Resources Domain

Part of the Communications in Computer and Information Science book series (CCIS,volume 637)

Abstract

We face the complex problem of timely, accurate and mutually satisfactory mediation between job offers and suitable applicant profiles by means of semantic processing techniques. In fact, this problem has become a major challenge for all public and private recruitment agencies around the world as well as for employers and job seekers. It is widely agreed that smart algorithms for automatically matching, learning, and querying job offers and candidate profiles will provide a key technology of high importance and impact and will help to counter the lack of skilled labor and/or appropriate job positions for unemployed people. Additionally, such a framework can support global matching aiming at finding an optimal allocation of job seekers to available jobs, which is relevant for independent employment agencies, e.g. in order to reduce unemployment.

Keywords

  • e-Recruitment
  • Knowledge engineering
  • Knowledge-based technology

The research reported in this paper was supported by the Austrian Forschungsforderungsgesellschaft (FFG) for the Bridge project Accurate and Efficient Profile Matching in Knowledge Bases (ACEPROM) under contract [FFG: 841284]. The research reported in this paper has been supported by the Austrian Ministry for Transport, Innovation and Technology, the Federal Ministry of Science, Research and Economy, and the Province of Upper Austria in the frame of the COMET center SCCH [FFG: 844597].

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  • DOI: 10.1007/978-3-319-44066-8_17
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Notes

  1. 1.

    http://www.disco-tools.eu.

  2. 2.

    http://www.ilo.org/public/english/bureau/stat/isco/isco08/index.htm.

  3. 3.

    http://www.uis.unesco.org/Education/Pages/international-standard-classification-of-education.aspx.

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Author information

Authors and Affiliations

  1. Software Competence Center Hagenberg GmbH, Softwarepark 21, 4232, Hagenberg, Austria

    Jorge Martinez-Gil, Alejandra Lorena Paoletti & Klaus-Dieter Schewe

Authors
  1. Jorge Martinez-Gil
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  2. Alejandra Lorena Paoletti
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  3. Klaus-Dieter Schewe
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Corresponding author

Correspondence to Jorge Martinez-Gil .

Editor information

Editors and Affiliations

  1. Faculty of Sciences, University of Novi Sad Faculty of Sciences, Novi Sad, Serbia

    Mirjana Ivanović

  2. Christian-Albrechts-Universität Kiel, Kiel, Germany

    Bernhard Thalheim

  3. University of Genoa, Genoa, Italy

    Barbara Catania

  4. Software Competence Cent. Hagenberg GmbH, Hagenberg, Austria

    Klaus-Dieter Schewe

  5. Riga Technical University, Riga, Latvia

    Mārīte Kirikova

  6. VSB-Technical University Ostrava, Ostrava, Czech Republic

    Petr Šaloun

  7. Georgia College and State University, Milledgeville, Georgia, USA

    Ajantha Dahanayake

  8. Politecnico di Torino, Torino, Italy

    Tania Cerquitelli

  9. Politecnico di Torino , Torino, Italy

    Elena Baralis

  10. EURECOM, Biot Sophia Antipolis cedex, France

    Pietro Michiardi

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Martinez-Gil, J., Paoletti, A.L., Schewe, KD. (2016). A Smart Approach for Matching, Learning and Querying Information from the Human Resources Domain. In: , et al. New Trends in Databases and Information Systems. ADBIS 2016. Communications in Computer and Information Science, vol 637. Springer, Cham. https://doi.org/10.1007/978-3-319-44066-8_17

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  • DOI: https://doi.org/10.1007/978-3-319-44066-8_17

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