document: Generalized fact-finding Once information retrieval has located a document , and information extraction has provided its contents , how do we know whether we should actually believe it ? Fact-finders are a state-of-the-art class of algorithms that operate in a manner analogous to Kleinberg 's Hubs and Authorities , iteratively computing the trustworthiness of an information source as a function of the believability of the claims it makes , and the believability of a claim as a function of the trustworthiness of those sources asserting it . However , as fact-finders consider only `` who claims what '' , they ignore a great deal of relevant background and contextual information . We present a framework for `` lifting '' ( generalizing ) the fact-finding process , allowing us to elegantly incorporate knowledge such as the confidence of the information extractor and the attributes of the information sources . Experiments demonstrate that leveraging this information significantly improves performance over existing , `` unlifted '' fact-finding algorithms .
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