| 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. |