We consider fact-checking approaches that aim to predict the veracity of assertions in knowledge graphs. Five main categories of fact-checking approaches for knowledge graphs have been proposed in the recent literature, of which each is subject to partially overlapping limitations. In particular, current text-based approaches are limited by manual feature engineering. Path-based and rule-based approaches are limited by their exclusive use of knowledge graphs as background knowledge, and embedding-based approaches suffer from low accuracy scores on current fact-checking tasks. We propose a hybrid approach -- dubbed HybridFC -- that exploits the diversity of existing categories of fact-checking approaches within an ensemble learning setting to achieve a significantly better prediction performance. In particular, our approach outperforms the state of the art by 0.14 to 0.27 in terms of Area Under the Receiver Operating Characteristic curve on the FactBench dataset. Our code is open-source and can be found at https://github.com/dice-group/HybridFC.

翻译：本文研究旨在预测知识图谱中断言真实性的各类事实核查方法。近期文献提出了知识图谱事实核查的五种主要方法类别，每种方法都存在部分重叠的局限性。具体而言，当前基于文本的方法受限于人工特征工程；基于路径和基于规则的方法因仅将知识图谱作为背景知识而存在局限；基于嵌入的方法在当前事实核查任务中面临准确率较低的困境。我们提出一种混合方法——命名为HybridFC——该方法在集成学习框架下综合利用现有各类事实核查方法的多样性，实现了显著更优的预测性能。特别地，在FactBench数据集上，我们的方法在接收者操作特征曲线下面积指标上以0.14至0.27的优势超越了现有最优方法。我们的代码已开源，可通过https://github.com/dice-group/HybridFC获取。