| 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-the-art fact-finding algorithms
demonstrate that generalized fact-finders achieve significantly
better performance than their original variants on both
semi-synthetic and real-world problems. |