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