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 in-formation extraction of claims from documents,attributes of the sources, the degree of similar-ity among claims, and the degree of certainty ex-pressed by the sources. We introduce a new,gen-eralized fact-finding framework able to incorpo-rate this additional information into the fact-finding process.  Experiments using several state-of-the-art fact-finding algorithms demonstrate that gener-alized fact-finders achieve significantly better per-formance than their original variants on both semi-synthetic and real-world problems.