  In keyword search, when user cannot get what she wants, query refinement is
needed and reason can be various. We first give a thorough categorization of
the reason, then focus on solving one category of query refinement problem in
the context of XML keyword search, where what user searches for does not exist
in the data. We refer to it as the MisMatch problem in this paper. Then we
propose a practical way to detect the MisMatch problem and generate helpful
suggestions to users. Our approach can be viewed as a post-processing job of
query evaluation, and has three main features: (1) it adopts both the suggested
queries and their sample results as the output to user, helping user judge
whether the MisMatch problem is solved without consuming all query results; (2)
it is portable in the sense that it can work with any LCA-based matching
semantics and orthogonal to the choice of result retrieval method adopted; (3)
it is lightweight in the way that it occupies a very small proportion of the
whole query evaluation time. Extensive experiments on three real datasets
verify the effectiveness, efficiency and scalability of our approach. An online
XML keyword search engine called XClear that embeds the MisMatch problem
detector and suggester has been built.
