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Inference networks for document retrieval
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The effectiveness of GIOSS for the text database discovery problem
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Pruning long documents for distributed information retrieval
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QProber: A system for automatic classification of hidden-Web databases
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EDGAR-analyzer: automating the analysis of corporate data contained in the SEC's EDGAR database
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Automatically Selecting Strategies for Multi-Case-Base Reasoning
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An evolutionary approach to query-sampling for heterogeneous systems
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From uncertain inference to probability of relevance for advanced IR applications
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The proliferation of searchable text databases on corporate networks and the Internet causes a database selection problem for many people. Algorithms such as gGLOSS and CORI can automatically select which text databases to search for a given information need, but only if given a set of resource descriptions that accurately represent the contents of each database. The existing techniques for a acquiring resource descriptions have significant limitations when used in wide-area networks controlled by many parties. This paper presents query-based sampling, a new technicque for acquiring accurate resource descriptions. Query-based sampling does not require the cooperation of resource providers, nor does it require that resource providers use a particular search engine or representation technique. An extensive set of experimental results demonstrates that accurate resource descriptions are crated, that computation and communication costs are reasonable, and that the resource descriptions do in fact enable accurate automatic dtabase selection.