  Entity resolution is the problem of reconciling database references
corresponding to the same real-world entities. Given the abundance of publicly
available databases that have unresolved entities, we motivate the problem of
query-time entity resolution quick and accurate resolution for answering
queries over such unclean databases at query-time. Since collective entity
resolution approaches --- where related references are resolved jointly ---
have been shown to be more accurate than independent attribute-based resolution
for off-line entity resolution, we focus on developing new algorithms for
collective resolution for answering entity resolution queries at query-time.
For this purpose, we first formally show that, for collective resolution,
precision and recall for individual entities follow a geometric progression as
neighbors at increasing distances are considered. Unfolding this progression
leads naturally to a two stage expand and resolve query processing strategy. In
this strategy, we first extract the related records for a query using two novel
expansion operators, and then resolve the extracted records collectively. We
then show how the same strategy can be adapted for query-time entity resolution
by identifying and resolving only those database references that are the most
helpful for processing the query. We validate our approach on two large
real-world publication databases where we show the usefulness of collective
resolution and at the same time demonstrate the need for adaptive strategies
for query processing. We then show how the same queries can be answered in
real-time using our adaptive approach while preserving the gains of collective
resolution. In addition to experiments on real datasets, we use synthetically
generated data to empirically demonstrate the validity of the performance
trends predicted by our analysis of collective entity resolution over a wide
range of structural characteristics in the data.
