Spelling Suggestion for XML Keyword Search Based on XSketch Synopsis
Sheng Li, Junhu Wang · 2013
We study the spelling suggestion problem for XML keyword search, which provides users with alternative queries that may better express users' search intention. In order to return the suggested queries more efficiently, we evaluate the quality of the query by estimating the selectivity and quality of each query pattern. The selectivity estimation is based on the XSketch synopsis, which summarizes the structure and value distribution of the original XML data source. We propose an approach to generating the top-K query candidates. Experiments with real datasets verifies the effectiveness and efficiency of our approach.