Explainable fact checking with probabilistic answer set programming

A challenge in fact checking is the ability to provide explanations for the claim validation. We present a computational method that uses reference information to verify claims and explain its assessments. Our solution exploits existing formal representation of knowledge to generate interpretable explanations for the fact checking decisions.