KGDetector: Detecting Chinese Sensitive Information via Knowledge Graph-Enhanced BERT. (19th May 2022)

• Record Type: Journal Article
• Authors: Cong, Kai; Li, Tao; Li, Beibei; Gao, Zhan; Xu, Yanbin; Gao, Fei
• Other Names: Peng Hao Academic Editor.
• Is Part Of: Security and communication networks. Volume 2022(2022)
• Publication Date: 2022-05-19
• Subjects: Computer networks -- Security measures -- Periodicals; Computer security -- Periodicals; Cryptography -- Periodicals; 005.805
• DOI: 10.1155/2022/4656837
• Languages: English
• ISSNs: 1939-0114

Abstract

Abstract : The Bidirectional Encoder Representations from Transformers (BERT) technique has been widely used in detecting Chinese sensitive information. However, existing BERT-based frameworks usually fail to emphasize key entities in the texts that contribute significantly to knowledge inference. To meet this gap, we propose a BERT and knowledge graph-based novel framework to detect Chinese sensitive information (named KGDetector). Specifically, we first train a pretrained knowledge graph-based Chinese entity embedding model to characterize entities in the Chinese textual inputs. Finally, we propose an effective framework KGDetector to detect Chinese sensitive information, which employs the knowledge graph-based embedding model and the CNN classification model. Extensive experiments on our crafted Chinese sensitive information dataset demonstrate that KGDetector can effectively detect Chinese sensitive information, outperforming existing baseline frameworks.