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A profile describes a set of properties, e.g. a set of skills a person mayhave, a set of skills required for a particular job, or a set of abilities afootball player may have with respect to a particular team strategy. Profilematching aims to determine how well a given profile fits to a requestedprofile. The approach taken in this article is grounded in a matching theorythat uses filters in lattices to represent profiles, and matching values in theinterval [0,1]: the higher the matching value the better is the fit. Suchlattices can be derived from knowledge bases exploiting description logics torepresent the knowledge about profiles. An interesting first question is, howhuman expertise concerning the matching can be exploited to obtain mostaccurate matchings. It will be shown that if a set of filters together withmatching values by some human expert is given, then under some mildplausibility assumptions a matching measure can be determined such that thecomputed matching values preserve the rankings given by the expert. A secondquestion concerns the efficient querying of databases of profile instances. Formatching queries that result in a ranked list of profile instances matching agiven one it will be shown how corresponding top-k queries can be evaluated ongrounds of pre-computed matching values, which in turn allows the maintenanceof the knowledge base to be decoupled from the maintenance of profileinstances. In addition, it will be shown how the matching queries can beexploited for gap queries that determine how profile instances need to beextended in order to improve in the rankings. Finally, the theory of matchingwill be extended beyond the filters, which lead to a matching theory thatexploits fuzzy sets or probabilistic logic with maximum entropy semantics. Itwill be shown that added fuzzy links can be captured by extending theunderlying lattice.

Accurate and Efficient Profile Matching in Knowledge Bases