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When one Sample is not Enough: Improving Text Database Selection Using Shrinkage

Ipeirotis, Panagiotis G.; Gravano, Luis

Database selection is an important step when searching over large numbers of distributed text databases. The database selection task relies on statistical summaries of the database contents, which are not typically exported by databases. Previous research has developed algorithms for constructing an approximate content summary of a text database from a small document sample extracted via querying. Unfortunately, Zipf's law practically guarantees that content summaries built this way for any relatively large database will fail to cover many low-frequency words. Incomplete content summaries might negatively affect the database selection process, especially for short queries with infrequent words. To improve the coverage of approximate content summaries, we build on the observation that topically similar databases tend to have related vocabularies. Therefore, the approximate content summaries of topically related databases can complement each other and increase their coverage. Specifically, we exploit a (given or derived) hierarchical categorization of the databases and adapt the notion of "shrinkage" --a form of smoothing that has been used successfully for document classification-- to the content summary construction task. A thorough evaluation over 315 real web databases as well as over TREC data suggests that the shrinkage-based content summaries are substantially more complete than their "unshrunk" counterparts. We also describe how to modify existing database selection algorithms to adaptively decide --at run-time-- whether to apply shrinkage for a query. Our experiments, which rely on TREC data sets, queries, and the associated "relevance judgments," show that our shrinkage-based approach is significantly more accurate than state-of-the-art database selection algorithms, including a recently proposed hierarchical strategy that also exploits database classification.

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Academic Units
Computer Science
Publisher
Department of Computer Science, Columbia University
Series
Columbia University Computer Science Technical Reports, CUCS-013-04
Published Here
April 26, 2011
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