Fact-checking is a crucial task as it ensures the prevention of misinformation. However, manual fact-checking cannot keep up with the rate at which false information is generated and disseminated online. Automated fact-checking by machines is significantly quicker than by humans. But for better trust and transparency of these automated systems, explainability in the fact-checking process is necessary. Fact-checking often entails contrasting a factual assertion with a body of knowledge for such explanations. An effective way of representing knowledge is the Knowledge Graph (KG). There have been sufficient works proposed related to fact-checking with the usage of KG but not much focus is given to the application of reinforcement learning (RL) in such cases. To mitigate this gap, we propose an RL-based KG reasoning approach for explainable fact-checking. Extensive experiments on FB15K-277 and NELL-995 datasets reveal that reasoning over a KG is an effective way of producing human-readable explanations in the form of paths and classifications for fact claims. The RL reasoning agent computes a path that either proves or disproves a factual claim, but does not provide a verdict itself. A verdict is reached by a voting mechanism that utilizes paths produced by the agent. These paths can be presented to human readers so that they themselves can decide whether or not the provided evidence is convincing or not. This work will encourage works in this direction for incorporating RL for explainable fact-checking as it increases trustworthiness by providing a human-in-the-loop approach.

翻译：事实验证是一项关键任务，因为它能确保防止错误信息的传播。然而，人工事实验证无法跟上虚假信息在线生成和传播的速度。机器自动事实验证比人类快得多。但为了提升这些自动化系统的可信度与透明度，事实验证过程中的可解释性必不可少。事实验证通常需要将事实断言与知识库进行对比以提供解释。知识图谱（KG）是知识表示的有效方式。目前已提出大量利用知识图谱进行事实验证的相关工作，但针对强化学习（RL）在此类场景中的应用关注甚少。为弥补这一空白，我们提出一种基于强化学习的知识图谱推理方法，用于可解释事实验证。在FB15K-277和NELL-995数据集上的大量实验表明，对知识图谱进行推理是生成以路径和分类形式呈现、可供人类理解的事实验证解释的有效方式。强化学习推理代理会计算出一条要么证明、要么证伪事实断言的路径，但其本身并不给出判定结果。判定结果由一种投票机制得出，该机制利用代理生成的路径。这些路径可呈现给人类读者，以便他们自行判定所提供的证据是否令人信服。本研究通过提供人机协作的方法增强了可信度，将推动将强化学习用于可解释事实验证的相关方向研究。