International Conference on Database and Expert Systems Applications

DEXA 2015: Database and Expert Systems Applications pp 21-35 | Cite as

Extending Knowledge-Based Profile Matching in the Human Resources Domain

  • Alejandra Lorena Paoletti
  • Jorge Martinez-Gil
  • Klaus-Dieter Schewe
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

References

  1. 1.
    European dictionary of skills and competences. http://www.disco-tools.eu
  2. 2.
  3. 3.
    International standard classification of occupations. http://www.ilo.org/public/english/bureau/stat/isco/isco08/index.htm
  4. 4.
    Baader, F., Bürckert, H.-J., Heinsohn, J., Hollunder, B., Müller, J., Nebel, B., Nutt, W., Profitlich, H.-J.: Terminological knowledge representation: a proposal for a terminological logic. In: Description Logics, pp. 120–128 (1991)Google Scholar
  5. 5.
    Baader, F., Calvanese, D., McGuinness, D.L., Nardi, D., Patel-Schneider, P.F. (eds.): The Description Logic Handbook: Theory, Implementation, and Applications. Cambridge University Press, Cambridge (2003)Google Scholar
  6. 6.
    Cimiano, P., Hotho, A., Stumme, G., Tane, J.: Conceptual knowledge processing with formal concept analysis and ontologies. In: Eklund, P. (ed.) ICFCA 2004. LNCS (LNAI), vol. 2961, pp. 189–207. Springer, Heidelberg (2004) CrossRefGoogle Scholar
  7. 7.
    Grau, B.C., Horrocks, I., Motik, B., Parsia, B., Patel-Schneider, P.F., Sattler, U.: OWL 2: The next step for OWL. J. Web Sem. 6(4), 309–322 (2008)CrossRefGoogle Scholar
  8. 8.
    Gruber, T.R.: Toward principles for the design of ontologies used for knowledge sharing? Int. J. Hum. Comput. Stud. 43(5–6), 907–928 (1995)CrossRefGoogle Scholar
  9. 9.
    Klein, M.C.A., Broekstra, J., Fensel, D., van Harmelen, F., Horrocks, I.: Ontologies and schema languages on the web. In: Spinning the Semantic Web: Bringing the World Wide Web to its Full Potential [outcome of a Dagstuhl seminar], pp. 95–139 (2003)Google Scholar
  10. 10.
    Looser, D., Ma, H., Schewe, K.-D.: Using formal concept analysis for ontology maintenance in human resource recruitment. In: Ninth Asia-Pacific Conference on Conceptual Modelling, APCCM 2013, Adelaide, Australia, January 29–Feburary 1 2013, pp. 61–68 (2013)Google Scholar
  11. 11.
    Mochol, M., Nixon, L.J.B., Wache, H.: Improving the recruitment process through ontology-based querying. In: Proceedings of the First International Workshop on Applications and Business Aspects of the Semantic Web (SEBIZ 2006) Collocated with the 5th International Semantic Web Conference (ISWC-2006), Athens, Georgia, USA, 6 November 2006 (2006)Google Scholar
  12. 12.
    Mochol, M., Wache, H., Nixon, L.J.B.: Improving the accuracy of job search with semantic techniques. In: Abramowicz, W. (ed.) BIS 2007. LNCS, vol. 4439, pp. 301–313. Springer, Heidelberg (2007) CrossRefGoogle Scholar
  13. 13.
    Popov, N., Jebelean, T.: Semantic matching for job search engines: a logical approach. Technical report 13–02, RISC Report Series. University of Linz, Austria (2013)Google Scholar
  14. 14.
    Zavitsanos, E., Paliouras, G., Vouros, G.A.: Gold standard evaluation of ontology learning methods through ontology transformation and alignment. IEEE Trans. Knowl. Data Eng. 23(11), 1635–1648 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Alejandra Lorena Paoletti
    • 1
  • Jorge Martinez-Gil
    • 1
  • Klaus-Dieter Schewe
    • 1
    • 2
  1. 1.Software Competence Center HagenbergHagenbergAustria
  2. 2.Research Institute for Applied Knowledge ProcessingJohannes-Kepler-UniversityLinzAustria

Personalised recommendations