Fact Checking from Natural Text with Probabilistic Soft Logic

Abstract

We demonstrate a method to support fact-checking of statements found in natural text such as online news, encyclopedias or academic repositories, by detecting if they violate knowledge that is implicitly present in a reference corpus. The method combines the use of information extraction techniques with probabilistic reasoning, allowing for inferences to be performed starting from natural text. We present two case studies, one in the domain of verifying claims about family relations, the other about political relations. This allows us to contrast the case where ground truth is available about the relations and the rules that can be applied to them (families) with the case where neither relations nor rules are clear cut (politics)