Knowledge Commons of Institute of Automation,CAS
A KG-based Enhancement Framework for Fact Checking Using Category Information | |
Wang S(王帅)1,2![]() ![]() ![]() | |
2020-11 | |
会议名称 | 2020 IEEE International Conference on Intelligence and Security Informatics (ISI) |
会议日期 | 2020年11月 |
会议地点 | 线上 |
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. |
关键词 | fact checking knowledge graph |
学科门类 | 工学 |
DOI | 10.1109/ISI49825.2020.9280520 |
收录类别 | EI |
语种 | 英语 |
引用统计 | |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/48951 |
专题 | 多模态人工智能系统全国重点实验室_互联网大数据与信息安全 |
通讯作者 | Wang L(王磊) |
作者单位 | 1.中国科学院大学 2.中国科学院自动化研究所 |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
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],2020. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
ISI20CameraReady3.pd(1222KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论