Authors:
Miljan Vučetić
1
and
Miroslav Hudec
2
Affiliations:
1
Vlatacom Institute of High Technologies, 5 Milutina Milankovića Blvd, Belgrade and Serbia
;
2
Faculty of Economic Informatics, University of Economics in Bratislava, Dolnozemská cesta 1, Bratislava and Slovakia
Keyword(s):
Similarity, Conformance Measure, Fuzzy Conjunction, Uni-norms, Geometric Mean, Quantified Fuzzy Aggregation.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Fuzzy Information Processing, Fusion, Text Mining
;
Fuzzy Systems
;
Soft Computing
Abstract:
Matching user preferences with content in datasets is an important task in building robust query engines. However, this is still a challenging task, because the entities’ attributes are often expressed by various data types including numerical, categorical, and fuzzy data. Moreover, the user’s preferences and data types for particular attributes may not collide, i.e. the user explains his requirements in linguistic term(s), whereas the respective attribute is recorded as a real number and vice versa. Further, the user may provide different relevancies for atomic conditions, where usual one-directional reinforcement aggregation functions, e.g. conjunction, are not suitable. In this paper, we propose a robust framework capable to manage user requirements and match them with records in a dataset. The former is solved by conformance measure, whereas for the latter the suitable aggregation functions have been suggested to cover particular aggregation needs. Finally, we discuss benefits, d
rawbacks and outline further activities.
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