In real world applications, knowledge graphs (KG) are widely used in various domains (e.g. medical applications and dialogue agents). However, for fact verification, KGs have not been adequately utilized as a knowledge source. KGs can be a valuable knowledge source in fact verification due to their reliability and broad applicability. A KG consists of nodes and edges which makes it clear how concepts are linked together, allowing machines to reason over chains of topics. However, there are many challenges in understanding how these machine-readable concepts map to information in text. To enable the community to better use KGs, we introduce a new dataset, FactKG: Fact Verification via Reasoning on Knowledge Graphs. It consists of 108k natural language claims with five types of reasoning: One-hop, Conjunction, Existence, Multi-hop, and Negation. Furthermore, FactKG contains various linguistic patterns, including colloquial style claims as well as written style claims to increase practicality. Lastly, we develop a baseline approach and analyze FactKG over these reasoning types. We believe FactKG can advance both reliability and practicality in KG-based fact verification.

翻译：在现实世界应用中，知识图谱被广泛应用于医疗应用和对话代理等多个领域。然而，在事实验证方面，知识图谱尚未被充分用作知识源。由于知识图谱的可靠性和广泛适用性，它们可以成为事实验证中宝贵的知识来源。知识图谱由节点和边构成，清晰展示了概念之间的关联方式，使机器能够沿着主题链进行推理。然而，理解这些机器可读概念如何映射到文本信息仍存在诸多挑战。为帮助学界更好地利用知识图谱，我们引入了新数据集FactKG：基于知识图谱推理的事实验证。该数据集包含10.8万个自然语言声明，涵盖五类推理类型：单跳、合取、存在性、多跳和否定。此外，FactKG包含口头语风格和书面语风格等多种语言模式，以提高其实用性。最后，我们开发了基线方法并针对这些推理类型对FactKG进行了分析。我们相信FactKG能够推动基于知识图谱的事实验证在可靠性和实用性方面的进步。