Detection of fake news has spurred widespread interests in areas such as healthcare and Internet societies, in order to prevent propagating misleading information for commercial and political purposes. However, efforts to study a general framework for exploiting knowledge, for judging the trustworthiness of given news based on their content, have been limited. Indeed, the existing works rarely consider incorporating knowledge graphs (KGs), which could provide rich structured knowledge for better language understanding.

In this work, we propose a deep triple network (DTN) that leverages knowledge graphs to facilitate fake news detection with triple-enhanced explanations. In the DTN, background knowledge graphs, such as open knowledge graphs and extracted graphs from news bases, are applied for both low-level and high-level feature extraction to classify the input news article and provide explanations for the classification.

The performance of the proposed method is evaluated by demonstrating abundant convincing comparative experiments. Obtained results show that DTN outperforms conventional fake news detection methods from different aspects, including the provision of factual evidence supporting the decision of fake news detection.

检测假新闻在医疗保健和互联网社会等领域引起了广泛关注，以防止出于商业和政治目的传播误导性信息。然而，研究利用知识的一般框架、根据内容判断给定新闻的可信度的努力是有限的。事实上，现有的工作很少考虑合并知识图谱（KGs），它可以提供丰富的结构化知识以更好地理解语言。

在这项工作中，我们提出了一个深度三重网络 (DTN)，它利用知识图来促进具有三重增强解释的假新闻检测。在 DTN 中，背景知识图，例如开放知识图和从新闻库中提取的图，被应用于低级和高级特征提取，以对输入的新闻文章进行分类并为分类提供解释。

通过展示大量令人信服的比较实验来评估所提出方法的性能。获得的结果表明，DTN 从不同方面优于传统的假新闻检测方法，包括提供支持假新闻检测决策的事实证据。