# A Review of Knowledge Graph-Based Reasoning Technology in the Operation of Power Systems
## 3 Applications of Knowledge Graphs in the Power System Operation
### Other Applications of KG in Power Operation  
In the intelligent Q&A system for power grid knowledge [129], heterogeneous data can be fused to initially construct KG. Then, the paths are sorted based on the idea of a path ranking algorithm (PRA) in rule mining to realize the reasoning and prediction of keyword entities and their relationships, providing users with answers and an intuitive analysis process. For specialized terms in the electric power field that are difficult for users to grasp, multidimensional semantic search [130] and intelligent recommendation methods [131] are used to mine the user's search intention and help users execute effective queries.  
In terms of power monitoring information, KG extracts the cumbersome base station information based on the characteristics and laws of the grid, equipment, and monitoring work. After filtering redundancy, the real fault information can be stored in Neo4j [132]. In Figure 6, we constructed a grid incident behavior KG based on monitoring signals of a small substation. In the KG, we can explore an effective \"event-based\" monitoring method by linking the abnormal information and process disposal of events. This method can avoid misjudgments and omissions caused by a human error through knowledge reasoning, help staff to complete the processing and prediction of most daily monitoring work and improve the efficiency of incident handling. In the future, we can connect monitoring and alert information to major business platforms to provide a comprehensive analysis of the basic data to assist and enhance the dynamic alert and risk warning capabilities of power grid operation.  
In the field of power metering, to construct KGs that unify terms across domains, it is necessary to fuse heterogeneous data from different sources and to fuzzify different descriptions of the same term. [PERSON]. [133] used a Bert-based knowledge extraction model to extract triadic information from the corpus. Then, the power metering KG was constructed based on text matching. Past power meter inspection methods were excessively dependent on the operator's skill. Recently, [PERSON]. [134] constructed power verification KG based on the RDF storage format and performed knowledge inference using the TransE model. Although  
Figure 5: Multiple KG coupling in the dispatching domain.  
the database and the inference model are slightly outdated compared to current mapping techniques in other fields, the application has improved the accuracy and efficiency of power meter inspection and protected the interests of power companies and customers.  
In the area of electric power safety regulation, two major challenges are being faced at present: the lack of entity and relationship data for electric power safety regulations and the complexity of inter-textual relationships. The current research is still stagnant in entity identification [135] and accurate extraction of entity relationships [136] in power safety regulations. As a key link in the field of electric power systems that cannot be ignored, a deeper study of power safety regulations KG will become the future research direction in the field of power safety.  
In addition to this, [PERSON]. [137] proposed an optimal KG-based penetration path scheme for when the power system is under attack. To promote the process of power system automation and reduce manual operations, the KG-based human-computer interaction model [138] uses the TextRank algorithm [139] for entity extraction and extracts logical relationship entities based on semantic rules. Knowledge fusion imports the ternary data results into the Neo4j graph database to form a KG that can be queried and used.