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Many applications today need to manage data that is uncertain, such as Information Extraction (IE), data integration, sensor RFID networks, and scientific experiments. Top-k queries are often natural and useful in analyzing uncertain data in those applications. In this paper, the authors study the problem of answering top-k queries in a probabilistic framework from a state-of-the-art statistical IE model-semi-Conditional Random Fields (CRFs)-in the setting of Probabilistic Databases that treat statistical models as first-class data objects.
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