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There is great interest in supporting imprecise queries (e.g., keyword searchor natural language queries) over databases today. To support such queries, thedatabase system is typically required to disambiguate parts of theuser-specified query against the database, using whatever resources areintrinsically available to it (the database schema, data values distributions,natural language models etc). Often, systems will also have a user-interactionlog available, which can serve as an extrinsic resource to supplement theirmodel based on their own intrinsic resources. This leads to a problem of howbest to combine the system's prior ranking with insight derived from theuser-interaction log. Statistical inference techniques such as maximumlikelihood or Bayesian updates from a subjective prior turn out not to apply ina straightforward way due to possible noise from user search behavior and toencoding biases endemic to the system's models. In this paper, we address suchlearning problem in interactive data retrieval, with specific focus on typeclassification for user-specified query terms. We develop a novel Bayesiansmoothing algorithm, Bsmooth, which is simple, fast, flexible and accurate. Weanalytically establish some desirable properties and show, through experimentsagainst an independent benchmark, that the addition of such a learning layerperforms much better than standard methods.

Bsmooth: Learning from user feedback to disambiguate query terms in interactive data retrieval