Top-k Matching Queries for Filter-Based Profile Matching in Knowledge Bases

  • Alejandra Lorena Paoletti
  • Jorge Martinez-Gil
  • Klaus-Dieter Schewe
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9828)


Finding the best matching job offers for a candidate profile or, the best candidates profiles for a particular job offer, respectively constitutes the most common and most relevant type of queries in the Human Resources (HR) sector. This technically requires to investigate top-k queries on top of knowledge bases and relational databases. We propose in this paper a top-k query algorithm on relational databases able to produce effective and efficient results. The approach is to consider the partial order of matching relations between jobs and candidates profiles together with an efficient design of the data involved. In particular, the focus on a single relation, the matching relation, is crucial to achieve the expectations.


Relational Database Matching Relation Applicant Profile Minimum Match Profile Match 
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.


  1. 1.
    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
  2. 2.
    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
  3. 3.
    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
  4. 4.
    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
  5. 5.
    Rácz, G., Sali, A., Schewe, K.-D.: Semantic matching strategies for job recruitment: a comparison of new and known approaches. In: Gyssens, M., Simari, G. (eds.) FoIKS 2016. LNCS, vol. 9616, pp. 149–168. Springer, Heidelberg (2016). doi: 10.1007/978-3-319-30024-5_9 CrossRefGoogle Scholar
  6. 6.
    Straccia, U., Madrid, N.: A top-k query answering procedure for fuzzy logic programming. Fuzzy Sets Syst. 205, 1–29 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Theobald, M., Weikum, G., Schenkel, R.: Top-k query evaluation with probabilistic guarantees. In: (e)Proceedings of the Thirtieth International Conference on Very Large Data Bases, Toronto, Canada, pp. 648–659, 31 August–3 September 2004Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

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

Personalised recommendations