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This content will become publicly available on December 17, 2023

Title: Comparative Reasoning for Knowledge Graph Fact Checking
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.  more » « less
Award ID(s):
2134079 1939725 1947135
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
2022 IEEE International Conference on Big Data (Big Data)
Page Range / eLocation ID:
2309 to 2312
Medium: X
Sponsoring Org:
National Science Foundation
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