Construction of Knowledge Graph Based on Discipline Inspection and Supervision

EasyChair Preprint 5608, version 2

Abstract

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 in the discipline inspection field established through examples provides support and assistance for the discipline inspection personnel's scientific decision-making.

Keyphrases: BERT, Knowledge Graph, NER, discipline inspection and supervision, graph database

@booklet{EasyChair:5608,
  author    = {Yuefeng Liu and Wei Guo and Hanyu Zhang and Haodong Bian and Yingjie He and Xiaoyan Zhang and Yanzhang Gong and Zhen Liu},
  title     = {Construction of Knowledge Graph Based on Discipline Inspection and Supervision},
  howpublished = {EasyChair Preprint 5608},
  year      = {EasyChair, 2021}}