Research on Machine Learning-based Knowledge Graph Completion Technique for Power Grid Contingency Planning

With the expansion of the scope of emergency security management work in the power grid, the knowledge in the field of emergency safety has gradually increased, and in the face of the numerous and complex knowledge, knowledge management is particularly important. With the rise and development of machine learning in the era of big data, knowledge graph is widely used in the field of grid emergency safety management. While the massive emergency plan text in the knowledge graph construction process, there is often the problem of missing entities, which needs to be complemented by the knowledge graph. Based on machine learning algorithms, this paper compares the performance of several knowledge graph complementation algorithms on a dataset to find out the optimal algorithm for the scenario.