Making better informed trust decisions with generalized fact-finding

Jeff Pasternack, Dan Roth

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

Abstract

Information retrieval may suggest a document, and information extraction may tell us what it says, but which information sources do we trust and which assertions do we believe when different authors make conflicting claims? Trust algorithms known as fact-finders attempt to answer these questions, but consider only which source makes which claim, ignoring a wealth of background knowledge and contextual detail such as the uncertainty in the information extraction of claims from documents, attributes of the sources, the degree of similarity among claims, and the degree of certainty expressed by the sources. We introduce a new, generalized fact-finding framework able to incorporate this additional information into the fact-finding process. Experiments using several state-of-theart fact-finding algorithms demonstrate that generalized fact-finders achieve significantly better performance than their original variants on both semi-synthetic and real-world problems.

Original languageEnglish (US)
Title of host publicationIJCAI 2011 - 22nd International Joint Conference on Artificial Intelligence
Pages2324-2329
Number of pages6
DOIs
StatePublished - 2011
Event22nd International Joint Conference on Artificial Intelligence, IJCAI 2011 - Barcelona, Catalonia, Spain
Duration: Jul 16 2011Jul 22 2011

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
ISSN (Print)1045-0823

Other

Other22nd International Joint Conference on Artificial Intelligence, IJCAI 2011
Country/TerritorySpain
CityBarcelona, Catalonia
Period7/16/117/22/11

ASJC Scopus subject areas

  • Artificial Intelligence

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