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.

本文介绍了一个基于知识图谱的事实检查方法，利用其中的语义表示和逻辑规则进行推理，以产生可解释的结果。实验表明，这种基于概率推理的方法比现有技术水平更具准确性。