Construction of Knowledge Graph Based on Discipline Inspection and Supervision
Yue‐Feng Liu, Wei Guo, Hanyu Zhang, Haodong Bian, HE Ying-jie, Xiaoyan Zhang, Yanzhang Gong, Jianmin Dong, Zhen Liu · 2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · 2021
To solve the problems of large number of notifications, low relevance and no relevant knowledge base in the field of discipline inspection, a method of constructing a knowledge map of discipline inspection and supervision based on the BERT-BiLSTM-CRF model is proposed. Firstly, the unstructured data is collected from the content of the disciplinary inspection and supervision report. Through the bottom-up method the notification concept layer is constructed. By using deep learning models to extract entities. Then the entities and semantic relations are stored in the graph database Neo4j and displayed in the form of a knowledge graph. This method realizes the whole process from unstructured data to knowledge graph, and provides technical reference for the construction of domain-based knowledge graph. Simultaneously, the knowledge map of discipline inspection field established through the example can find the hidden association between those who break the law and discipline, prevent criminal facts in advance, and provide support and help for discipline inspection personnel to implement the spirit of the eight-point regulation of the Central Committee and continue to fight against the “four winds” and other actions.