A KG-based Enhancement Framework for Fact Checking Using Category Information | |
Wang S(王帅)1,2; Wang L(王磊)1,2; Mao WJ(毛文吉)1,2 | |
2020-11 | |
会议日期 | 2020年11月 |
会议地点 | 线上 |
关键词 | fact checking knowledge graph |
DOI | 10.1109/ISI49825.2020.9280520 |
英文摘要 | The massive spread of false information has brought about severe security-related problems to individuals and society. To debunk misinformation automatically, fact checking has become an important task that aims at retrieving evidence from external sources to verify the truthfulness of a given claim. As knowledge graph (KG) is a classic external source for retrieving relevant evidence. Previous methods typically check a claim by making inferences over it. Entity category information can be utilized to strengthen both the learning and verification process. However, this information was largely ignored in previous research. To make better use of the category information, in this paper, we propose a category-based framework for improving the performance of fact checking with KGs. We first learn prototypes for each category as their representatives, and then propose a prototype-based learning technique for effectively modeling the entity dependency in KG. We further develop a prototype matching technique to explore the category-level relations between head and tail entities for more robust verification. Experimental results on two benchmark datasets and a real-world dataset show that our framework can significantly improve the reasoning abilities of KG reasoning methods on Fact Checking task. |
语种 | 英语 |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/48951] ![]() |
专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_互联网大数据与安全信息学研究中心 |
通讯作者 | Wang L(王磊) |
作者单位 | 1.中国科学院自动化研究所 2.中国科学院大学 |
推荐引用方式 GB/T 7714 | Wang S,Wang L,Mao WJ. A KG-based Enhancement Framework for Fact Checking Using Category Information[C]. 见:. 线上. 2020年11月. |
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