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 Knowledge Structure Driven Prototype Learning and Verification for Fact-Checking 相关领域
计算机科学
人工智能
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Abstract To inhibit the spread of rumorous information, fact checking aims at retrieving relevant evidence to verify the veracity of a given claim. Previous work on fact checking typically uses knowledge graphs (KGs) as external repositories and develop reasoning methods to retrieve evidence from KGs. Domain knowledge structure, including category hierarchy and attribute relationships, can be utilized as discriminative information to facilitate KG based learning and verification. However, in previous fact checking research, category hierarchy and attribute information was often scattered in a KG and treated as the ordinary triple facts in the learning process like other types of information, or was utilized in a limited way without the consideration of category hierarchy or the combination of category hierarchy with the learning process. Thus to better utilize category hierarchy and attribute relationships, |
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