A knowledge graph representation learning based approach to qualitative case discipline

To solve the problem of irregular and inefficient case characterization and discipline in the field of discipline inspection and supervision, a method of case characterization and discipline measurement based on knowledge map representation learning is proposed. The method uses the knowledge map of discipline inspection cases as auxiliary information, and the knowledge representation learning model is used to characterize the entities and relationships in the knowledge graph, and the results of the characterized representation are subsequently processed by similar case calculation, and then uses the results of the representation to calculate similar cases to further realize the qualitative discipline of cases. The negative sampling method of the knowledge representation learning model is investigated to improve the knowledge graph vectorization accuracy and facilitate the manipulation of the knowledge graph in the case of qualitative quantitative discipline task. The knowledge graph inference-based approach to case characterization and discipline improves accuracy and computational efficiency. By improving the existing model, the improved model can reduce the impact of noisy data to a certain extent.