# Knowledge-Graph-Based Integrated Line Loss Evaluation Management System
## 2 Constructing a Knowledge Graph for Comprehensive Line Loss Evaluation Management  
In order to address the issues associated with line loss indicators, it is essential to undertake two key steps: knowledge acquisition and knowledge extraction within the knowledge graph. In the field of power systems, which are characterized by complex knowledge systems, researchers have recently begun to explore the potential of knowledge graphs as a means of visualizing knowledge and enhancing the efficiency with which knowledge is utilized. In their paper, [PERSON] [13] and others proposed multiple data processing and demonstrated the knowledge learning and mapping construction process. However, they did not indicate the role of the knowledge graph in the reference scheme or its use as a decision-making tool to improve the operational efficiency of the power system. Similarly, [PERSON] et al. [14] proposed the role of KG in the fault diagnosis of the power system with the aim of improving the operational efficiency of the power system. [PERSON] et al. [15] conducted a systematic review and summary of the application of KG in power system fault diagnosis and processing, with the aim of providing sufficient and comprehensive guidance for further research in this field. [PERSON] [16] reviewed the knowledge graph technology from the perspectives of knowledge extraction, knowledge representation, knowledge mining, knowledge reasoning, knowledge fusion, and the application of knowledge graphs. The review included examples of vertical risk control,operation mode analysis, optimization models, and overshooting parameters. However, the study lacked a correlation test of the data.  
A review of existing research findings reveals that, although the technical architectures of domain knowledge mapping construction employed by different research teams exhibit slight differences, the core modules encompass knowledge extraction, knowledge fusion, knowledge processing, and other components [17]. Accordingly, this paper employs the entity information extraction method of principal component analysis to perform data dimensionality reduction, feature extraction, data compression, and denoising on a substantial number of data indicators. A comprehensive evaluation of the entity mapping is conducted to characterize the actual grid network topology planning line loss, as well as the management, operation, technology, and other line losses. This is employed to facilitate entity query and matching services for the evaluation logic mapping.  
The index elements identified through principal component analysis inform the generation of evaluation logic maps. The evaluation logic map, as defined in this paper, represents a networked and structured expression of the specific evaluation logic within the traditional evaluation system. It places emphasis on the evaluation indexes, index weights, comprehensive evaluation criteria, and other relevant elements. The generation of the evaluation logic map is guided by the evaluation elements defined in the data model. This allows the application of natural language processing, knowledge extraction, information structuring and other technological means, such as the definition of the evaluation indexes and the method of calculating the indexes. Furthermore, the application of natural language processing, knowledge extraction, information structuring, and other technical means allows the specific contents, such as the definition of evaluation indexes, the calculation method of indexes, the design process of index weights, and the evaluation criteria model, to be obtained.  
Figure 1 depicts the methodology employed in the construction of the comprehensive evaluation management knowledge graph for line loss.  
Calculation and Analysis of Data in the Comprehensive Evaluation of Line Loss Management Entity Mapping