Generalized fact-finding

Jeff Pasternack, Dan Roth

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings of the 20th International Conference Companion on World Wide Web, WWW 2011
Pages99-100
Number of pages2
DOIs
StatePublished - Apr 29 2011
Event20th International Conference Companion on World Wide Web, WWW 2011 - Hyderabad, India
Duration: Mar 28 2011Apr 1 2011

Publication series

NameProceedings of the 20th International Conference Companion on World Wide Web, WWW 2011

Other

Other20th International Conference Companion on World Wide Web, WWW 2011
CountryIndia
CityHyderabad
Period3/28/114/1/11

Keywords

  • data integration
  • fact-finders
  • graph algorithms
  • trust

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems

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