Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Truth Discovery with Multiple Conflicting Information Providers on the Web
IEEE Transactions on Knowledge and Data Engineering
Corroborating information from disagreeing views
Proceedings of the third ACM international conference on Web search and data mining
Knowing what to believe (when you already know something)
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Heterogeneous network-based trust analysis: a survey
ACM SIGKDD Explorations Newsletter
Making better informed trust decisions with generalized fact-finding
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
DeFacto - deep fact validation
ISWC'12 Proceedings of the 11th international conference on The Semantic Web - Volume Part I
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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.