KGDetector: Detecting Chinese Sensitive Information via Knowledge Graph-Enhanced BERT

Cong, Kai; Li, Beibei; Li, Beibei; Gao, Zhan; Xu, Yanbin; Gao, Fei

doi:10.1155/2022/4656837

Cited by 2 publications

(1 citation statement)

References 22 publications

(34 reference statements)

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“…A knowledge graph is a kind of knowledge system that represents knowledge in a structured way and is mainly used to describe various entities and concepts that exist in the real world and the relationships among them. Many studies combine knowledge graphs with pre-trained models or deep neural networks to achieve text classification [22][23][24][25][26]. Zhao et al [27] solved the problem of small data samples by injecting sentiment domain knowledge into a linguistic representation model to exploit the additional information in sentiment knowledge graphs and obtain the embedding vectors of entities in sentiment knowledge graphs and words in a text in a consistent vector space.…”

Section: Related Workmentioning

confidence: 99%

Knowledge-Graph- and GCN-Based Domain Chinese Long Text Classification Method

Wang

et al. 2023

Applied Sciences

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In order to solve the current problems in domain long text classification tasks, namely, the long length of a document, which makes it difficult for the model to capture key information, and the lack of expert domain knowledge, which leads to insufficient classification accuracy, a domain long text classification model based on a knowledge graph and a graph convolutional neural network is proposed. BERT is used to encode the text, and each word’s corresponding vector is used as a node for the graph convolutional neural network so that the initialized vector contains rich semantic information. Using the trained entity–relationship extraction model, the entity-to-entity–relationships in the document are extracted and used as the edges of the graph convolutional neural network, together with syntactic dependency information. The graph structure mask is used to learn about edge relationships and edge types to further enhance the learning ability of the model for semantic dependencies between words. The method further improves the accuracy of domain long text classification by fusing knowledge features and data features. Experiments on three long text classification datasets—IFLYTEK, THUCNews, and the Chinese corpus of Fudan University—show accuracy improvements of 8.8%, 3.6%, and 2.6%, respectively, relative to the BERT model.

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Section: Related Workmentioning

confidence: 99%

Knowledge-Graph- and GCN-Based Domain Chinese Long Text Classification Method

Wang

et al. 2023

Applied Sciences

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Research on Intelligent Perception Algorithm for Sensitive Information

Huo

Jiang

2023

Applied Sciences

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In the big data era, a tremendous volume of electronic documents is transmitted via the network, many of which include sensitive information about the country and businesses. There is a pressing need to be able to perform intelligent sensing of sensitive information on these documents in order to be able to discover and guarantee the security of sensitive information in this enormous volume of documents. Although the low effectiveness of manual detection is resolved by the current method of handling sensitive information, there are still downsides, such as poor processing effects and slow speed. This study creatively proposes the Text Sensitive Information Intelligent Perception algorithm (TSIIP), which detects sensitive words at the word level and sensitive statements at the statement level to obtain the final assessment score of the text. We experimentally compare this algorithm with other methods on an existing dataset of sensitive Chinese information. We use the metrics measuring the accuracy of the binary classification model, where the F1 score reaches 0.938 (+0.6%), and the F2 score reaches 0.946 (+1%), and the experimental results fully demonstrate the superiority of this algorithm.

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KGDetector: Detecting Chinese Sensitive Information via Knowledge Graph-Enhanced BERT

Cited by 2 publications

References 22 publications

Knowledge-Graph- and GCN-Based Domain Chinese Long Text Classification Method

Knowledge-Graph- and GCN-Based Domain Chinese Long Text Classification Method

Research on Intelligent Perception Algorithm for Sensitive Information

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