Explainable Fact Checking with Probabilistic Answer Set Programming
Naser Ahmadi, Joohyung Lee, Paolo Papotti, Mohammed Saeed · 2019
One challenge in fact checking is the ability to improve the transparency of the decision.We present a fact checking method that uses reference information in knowledge graphs (KGs) to assess claims and explain its decisions.KGs contain a formal representation of knowledge with semantic descriptions of entities and their relationships.We exploit such rich semantics to produce interpretable explanations for the fact checking output.As information in a KG is inevitably incomplete, we rely on logical rule discovery and on Web text mining to gather the evidence to assess a given claim.Uncertain rules and facts are turned into logical programs, and the checking task is modeled as an inference problem in a probabilistic extension of answer set programs.Experiments show that the probabilistic inference enables the efficient labeling of claims with interpretable explanations, and the quality of the results is higher than state-of-the-art baselines.