Comparative Reasoning for Knowledge Graph Fact Checking

Lihui Liu, Houxiang Ji, Jiejun Xu, Hanghang Tong

Abstract: Knowledge graph has been widely used in fact checking, owing to its capability to provide crucial background knowledge to help verify claims. Traditional fact checking works mainly focus on analyzing a single claim but have largely ignored analysis on the semantic consistency of pair-wise claims, despite its key importance in the real-world applications, e.g., multimodal fake news detection. This paper proposes a graph neural network based model INSPECTOR for pair-wise fact checking. Given a pair of claims, INSPECTOR aims to detect the potential semantic inconsistency of the input claims. The main idea of INSPECTOR is to use a graph attention neural network to learn a graph embedding for each claim in the pair, then use a tensor neural network to classify this pair of claims as consistent vs. inconsistent. The experiment results show that our algorithm outperforms state-of-the-art methods, with a higher accuracy and a lower variance.