Knowledge Enhanced Fact Checking and Verification

Abstract

As the Internet and social media offer increasing opportunities for organizations and individuals to publicize online contents, it has become essential to develop effective means to identify misinformation like fake news. Recently, fact checking systems have been regarded as a promising tool to automatically deal with large amounts of information. How to effectively take advantage of existing unstructured document knowledge bases and structured knowledge graphs to build robust fact checking systems, however, remains to be a challenge. In this paper, we propose a knowledge enhanced fact checking system, which leverages the Wikidata5M knowledge graph and Wikipedia documents to incorporate external knowledge into the claim to be checked for more robust and accurate fact checking. First, we devise a contextualized knowledge graph selection method to identify the most relevant sub-graph with the checked claim from the large knowledge graph. We then construct a novel claim-evidence-knowledge graph and use a graph attention network to integrate natural language evidence with structured knowledge triplets by allowing them to propagate information among each other. By integrating the claim, retrieved evidence and selected knowledge triplets in a unified claim-evidence-knowledge graph, our method improves the label accuracy of predicted claims by more than 4% on the FEVER dataset over state-of-the-art fact checking models.