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

  • Jorge Martinez-Gil
  • Alejandra Lorena Paoletti
  • Klaus-Dieter Schewe
Conference paper
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 

References

  1. 1.
    Bizer, C., Heese, R., Mochol, M., Oldakowski, R., Tolksdorf, R., Eckstein, R.: The impact of semantic web technologies on job recruitment processes. Wirtschaftsinformatik 2005, pp. 1367–1382 (2005)Google Scholar
  2. 2.
    Bradley, K., Smyth, B.: Personalized information ordering: a case study in online recruitment. Knowl. Based Syst. 16(5–6), 269–275 (2003)CrossRefMATHGoogle Scholar
  3. 3.
    Cali, A., Calvanese, D., Colucci, S., Di Noia, T., Donini, F.M.: A logic-based approach for matching user profiles. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds.) KES 2004. LNCS (LNAI), vol. 3215, pp. 187–195. Springer, Heidelberg (2004)Google Scholar
  4. 4.
    Chakrabarti, K., Ortega-Binderberger, M., Mehrotra, S., Porkaew, K.: Evaluating refined queries in top-k retrieval systems. IEEE Trans. Knowl. Data Eng. 16(2), 256–270 (2004)CrossRefGoogle Scholar
  5. 5.
    Colucci, S., Di Noia, T., Di Sciascio, E., Donini, F.M., Mongiello, M., Mottola, M.: A formal approach to ontology-based semantic match of skills descriptions. J. UCS 9(12), 1437–1454 (2003)Google Scholar
  6. 6.
    Faliagka, E., Tsakalidis, A.K., Tzimas, G.: An integrated E-recruitment system for automated personality mining and applicant ranking. Internet Res. 22(5), 551–568 (2012)CrossRefGoogle Scholar
  7. 7.
    Farber, F., Weitzel, T., Keim, T.: An automated recommendation approach toselection in personnel recruitment. In: AMCIS 2003, p. 302 (2003)Google Scholar
  8. 8.
    Garcia Sanchez, F., Martinez-Bejar, R., Contreras, L., Fernandez-Breis, J.T., Castellanos Nieves, D.: An ontology-based intelligent system for recruitment. Expert Syst. Appl. 31(2), 248–263 (2006)CrossRefGoogle Scholar
  9. 9.
    Chaves-Gonzalez, J.M., Martinez-Gil, J.: Evolutionary algorithm based on different semantic similarity functions for synonym recognition in the biomedical domain. Knowl. Based Syst. 37, 62–69 (2013)CrossRefGoogle Scholar
  10. 10.
    Ilyas, I.F., Aref, W.G., Elmagarmid, A.K.: Supporting top-k join queries in relational databases. VLDB J. 13(3), 207–221 (2004)CrossRefGoogle Scholar
  11. 11.
    Joachims, T.: The value of user feedback. In: Clough, P., Foley, C., Gurrin, C., Jones, G.J.F., Kraaij, W., Lee, H., Mudoch, V. (eds.) ECIR 2011. LNCS, vol. 6611, p. 6. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  12. 12.
    Kessler, R., Bechet, N., Roche, M., Torres-Moreno, J.M., El-Beze, M.: A hybrid approach to managing job offers and candidates. Inf. Process. Manage. 48(6), 1124–1135 (2012)CrossRefGoogle Scholar
  13. 13.
    Kuokka, D., Harada, L.: Integrating information via matchmaking. J. Intell. Inf. Syst. 6(2/3), 261–279 (1996)CrossRefGoogle Scholar
  14. 14.
    Lv, Y., Zhai, C.X.: A log-logistic model-based interpretation of TF normalization of BM25. In: Baeza-Yates, R., de Vries, A.P., Zaragoza, H., Cambazoglu, B.B., Murdock, V., Lempel, R., Silvestri, F. (eds.) ECIR 2012. LNCS, vol. 7224, pp. 244–255. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  15. 15.
    Malinowski, J., Keim, T., Wendt, O., Weitzel, T.: Matching people, jobs: a bilateral recommendation approach. In: HICSS 2006 (2006)Google Scholar
  16. 16.
    Martinez-Gil, J., Aldana-Montes, J.F.: Reverse ontology matching. SIGMOD Rec. 39(4), 5–11 (2010)CrossRefGoogle Scholar
  17. 17.
    Martinez-Gil, J., Aldana-Montes, J.F.: Evaluation of two heuristic approaches to solve the ontology meta-matching problem. Knowl. Inf. Syst. 26(2), 225–247 (2011)CrossRefGoogle Scholar
  18. 18.
    Martinez-Gil, J.: An overview of knowledge management techniques for e-recruitment. JIKM 13(2), 1450014 (2014)Google Scholar
  19. 19.
    Martinez-Gil, J.: Automated knowledge base management: a survey. Comput. Sci. Rev. 18, 1–9 (2015)CrossRefMATHMathSciNetGoogle Scholar
  20. 20.
    Mylopoulos, J., Brodie, M.L.: Knowledge bases and databases: current trends and future directions. In: Karagiannis, D. (ed.) IS/KI 1990 and KI-WS 1990. LNCS, vol. 474, pp. 153–180. Springer, Heidelberg (1991)CrossRefGoogle Scholar
  21. 21.
    Meo, P., Quattrone, G., Terracina, G., Ursino, D.: An XML-based multiagent system for supporting online recruitment services. IEEE Trans. Syst. Man Cybern. Part A 37(4), 464–480 (2007)CrossRefGoogle Scholar
  22. 22.
    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
  23. 23.
    Paoletti, A.L., Martinez-Gil, J., Schewe, K.-D.: Extending knowledge-based profile matching in the human resources domain. In: Chen, Q., Hameurlain, A., Toumani, F., Wagner, R., Decker, H. (eds.) DEXA 2015. LNCS, vol. 9262, pp. 21–35. Springer, Heidelberg (2015)CrossRefGoogle Scholar
  24. 24.
    Rácz, G., Sali, A., Schewe, K.-D.: Semantic matching strategies for job recruitment: a comparison of new and known approaches. In: Gyssens, M., et al. (eds.) FoIKS 2016. LNCS, vol. 9616, pp. 149–168. Springer, Heidelberg (2016). doi:10.1007/978-3-319-30024-5_9 CrossRefGoogle Scholar
  25. 25.
    Robertson, S.E., Walker, S., Hancock-Beaulieu, M.: Experimentation as a way of life: okapi at TREC. Inf. Process. Manage. 36(1), 95–108 (2000)CrossRefGoogle Scholar
  26. 26.
    Soliman, M.A., Ilyas, I.F., Ben-David, S.: Supporting ranking queries on uncertain and incomplete data. VLDB J. 19(4), 477–501 (2010)CrossRefGoogle Scholar
  27. 27.
    Straccia, U., Tinelli, E., Colucci, S., Di Noia, T., Di Sciascio, E.: A system for retrieving top-k candidates to job positions. In: Description Logics 2009 (2009)Google Scholar
  28. 28.
    Theobald, M., Weikum, G., Schenkel, R.: Top-k query evaluation with probabilistic guarantees. In: VLDB, pp. 648–659 (2011)Google Scholar
  29. 29.
    Thielsch, M.T., Traumer, L., Pytlik, L.: E-recruiting and fairness: the applicant’s point of view. Inf. Technol. Manage. (ITM) 13(2), 59–67 (2012)CrossRefGoogle Scholar
  30. 30.
    Tinelli, E., Cascone, A., Ruta, M., Di Noia, T., Di Sciascio, E., Donini, F.: An innovative semantic-based skill management system exploiting standard SQL. In: ICEIS (2), pp. 224–229 (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Jorge Martinez-Gil
    • 1
  • Alejandra Lorena Paoletti
    • 1
  • Klaus-Dieter Schewe
    • 1
  1. 1.Software Competence Center Hagenberg GmbHHagenbergAustria

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