A preference-based approach for interactive weight learning: learning weights within a logic-based query language

  • Authors:
  • David Zellhöfer;Ingo Schmitt

  • Affiliations:
  • Department of Computer Science, Brandenburg University of Technology Cottbus, Cottbus, Germany 03013;Department of Computer Science, Brandenburg University of Technology Cottbus, Cottbus, Germany 03013

  • Venue:
  • Distributed and Parallel Databases
  • Year:
  • 2010

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Abstract

The result quality of queries incorporating impreciseness can be improved by the specification of user-defined weights. Existing approaches evaluate weighted queries by applying arithmetic evaluations on top of the query's intrinsic logic. This complicates the usage of logic-based optimization. Therefore, we suggest a weighting approach that is completely embedded in a logic.In order to facilitate the user interaction with the system, we exploit the intuitively comprehensible concept of preferences. In addition, we use a machine-based learning algorithm to learn weighting values in correspondence to the user's intended semantics of a posed query. Experiments show the utility of our approach.