Accurate and Efficient Profile Matching in Knowledge Bases

A profile describes a set of properties, e.g. a set of skills a person may have, a set of skills required for a particular job, or a set of abilities a football player may have with respect to a particular team strategy. Profile matching aims to determine how well a given profile fits to a requested profile and vice versa. The approach taken in this article is grounded in a matching theory that uses filters in lattices to represent profiles, and matching values in the interval [0,1]: the higher the matching value the better is the fit. Such lattices can be derived from knowledge bases exploiting description logics to represent the knowledge about profiles. An interesting first question is, how human expertise concerning the matching can be exploited to obtain most accurate matchings. It will be shown that if a set of filters together with matching values by some human expert is given, then under some mild plausibility assumptions a matching measure can be determined such that the computed matching values preserve the relevant rankings given by the expert. A second question concerns the efficient querying of databases of profile instances. For matching queries that result in a ranked list of profile instances matching a given one it will be shown how corresponding top-k queries can be evaluated on grounds of pre-computed matching values, which in turn allows the maintenance of the knowledge base to be decoupled from the maintenance of profile instances. In addition, it will be shown how the matching queries can be exploited for gap queries that determine how profile IThe 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) (FFG: [841284]). The research 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]). We are further grateful to the co-funding support by two companies 3S and OntoJob in the frame of the ACEPROM project. ∗Corresponding author Email addresses: [email protected] (Jorge Martinez Gil), [email protected] (Alejandra Lorena Paoletti), [email protected] (Gábor Rácz), [email protected] (Attila Sali), [email protected] (Klaus-Dieter Schewe) Preprint submitted to Data & Knowledge Engineering November 21, 2017 ar X iv :1 70 6. 06 94 4v 3 [ cs .L O ] 1 7 N ov 2 01 7 instances need to be extended in order to improve in the rankings. Finally, the theory of matching will be extended beyond the filters, which lead to a matching theory that exploits fuzzy sets or probabilistic logic with maximum entropy semantics.