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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)

Abstract

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.

Keywords

  • 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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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. https://doi.org/10.1007/978-3-319-45547-1_11

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  • DOI: https://doi.org/10.1007/978-3-319-45547-1_11

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