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Maintenance of Profile Matchings in Knowledge Bases

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Part of the Lecture Notes in Computer Science book series (LNPSE,volume 9893)


A profile describes a set of properties, e.g. a set of skills a person may have or a set of skills required for a particular job. Profile matching aims to determine how well a given profile fits to a requested profile. Profiles can be defined by filters in a lattice of concepts derived from a knowledge base that is grounded in description logic, and matching can be realised by assigning values in [0,1] to pairs of such filters: the higher the matching value the better is the fit. In this paper the problem is investigated, whether given a set of filters together with matching values determined by some human expert a matching measure can be determined such that the computed matching values preserve the rankings given by the expert. In the paper plausibility constraints for the values given by an expert are formulated. If these plausibility constraints are satisfied, the problem of determining a ranking-preserving matching measure can be solved.


  • Profile Matching
  • Matching Measure
  • Matching Values
  • Plausible Constraints
  • Human Experts

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 [FFG: 841284]. The research reported in this paper has further 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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  1. Baader, F., et al. (eds.): The Description Logic Handbook: Theory, Implementation and Applications. Cambridge University Press, New York (2003)

    MATH  Google Scholar 

  2. European distionary of skills and competences.

  3. Falk, T., et al.: Semantic-web-technologien in der Arbeitsplatzvermittlung. Informatik Spektrum 29(3), 201–209 (2006)

    CrossRef  Google Scholar 

  4. Ganter, B., Wille, R.: Formal Concept Analysis - Mathematical Foundations. Springer, Heidelberg (1999)

    CrossRef  MATH  Google Scholar 

  5. Grau, B.C., Horrocks, I., Motik, B., Parsia, B., Patel-Schneider, P.F., Sattler, U.: OWL 2: the next step for OWL. J. Web Semant. 6(4), 309–322 (2008)

    CrossRef  Google Scholar 

  6. International Standard Classification of Education.

  7. International Standard Classification of Occupations (2008).

  8. Levandowsky, M., Winter, D.: Distance between sets. Nature 234(5), 34–35 (1971)

    CrossRef  Google Scholar 

  9. Looser, D., Ma, H., Schewe, K.-D.: Using formal concept analysis for ontology maintenance in human resource recruitment. In: Ferrarotti, F., Grossmann, G. (eds.) Ninth Asia-Pacific Conference on Conceptual Modelling (APCCM 2013), vol. 143. CRPIT, pp. 61–68. Australian Computer Society (2013)

    Google Scholar 

  10. 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)

    CrossRef  Google Scholar 

  11. 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)

    CrossRef  Google Scholar 

  12. Paoletti, A.L., Martinez-Gil, J., Schewe, K.-D.: Top-k matching queries for filter-based profile matching in knowledge bases. In: Ma, H., Hartmann, S. (eds.) Database and Expert Systems Applications (DEXA 2016), LNCS. Springer, Heidelberg (2016, to appear)

    Google Scholar 

  13. Popov, N., Jebelean, T.: Semantic matching for job search engines - a logical approach. Technical report 13–02, Research Institute for Symbolic Computation, JKU Linz (2013)

    Google Scholar 

  14. 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

    CrossRef  Google Scholar 

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Correspondence to Klaus-Dieter Schewe .

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Martinez-Gil, J., Paoletti, L., Rácz, G., Sali, A., Schewe, KD. (2016). Maintenance of Profile Matchings in Knowledge Bases. In: Bellatreche, L., Pastor, Ó., Almendros Jiménez, J., Aït-Ameur, Y. (eds) Model and Data Engineering. MEDI 2016. Lecture Notes in Computer Science(), vol 9893. Springer, Cham.

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