# A Review of Knowledge Graph-Based Reasoning Technology in the Operation of Power Systems
## 3 Applications of Knowledge Graphs in the Power System Operation
### Power System Operation Application Scenarios for the KG
#### 3.2.2 Power Customer Service  
From manual consultation to the use of mobile customer service applications, customer service in the power domain is undergoing a transition from manual to intelligent service. Customer service applications on mobile can overcome geographical barriers and provide standardized services to customers. However, for demands that are hard to identify, human customer service is still required online. Manual customer service has varying response efficiency and personal experience in practice, making it difficult to ensure customer satisfaction. As an important part of power marketing, building a KG for the customer service domain to provide back-end support for mobile applications is a breakthrough in the field of intelligent customer service [106]. By extracting knowledge from dialogue business processes, voice information, and customer service experience and combining it \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Precision/\%** & **Recall/\%** & **F1/\%** \\ \hline LSTM & 70.8 & 69.1 & 70.0 \\ Bi-LSTM & 71.3 & 74.1 & 72.7 \\ Bi-LSTM-CRF & 74.2 & 76.3 & 75.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Model evaluation of LSTM, Bi-LSTM, Bi-LSTM-CRF. with structured standard Q&A pairs in customer service KB, the power customer service KG is built with both top-down and bottom-up methods. The service process combined with the neural network model provides users with business consultation, fault recovery, and user satisfaction surveys, and other functions accurately and effectively locate their requests, realize efficient knowledge management [107], knowledge Q&A [108; 109], and recommendation [110]. Therefore, intelligent customer service has become a research hotspot in the electric power field. The understanding of customer intent is a difficult issue for power customer service KG. Unlike the above two scenarios, which can be handled directly by the KG, customers do not understand the expressions of terminology in the field of power. Without a clear problem description, it is difficult to query the corresponding solution. As a customer-oriented service industry, power marketing inevitably involves understanding user intent in human-machine interaction. How to identify the user's intent from the conversation and represent the intent in a trivial form that can be understood by KG is the key to the successful application of customer service KG. The traditional method is similar to NER. For example, [111] segmented the business request text to obtain a splitting of the utterance text. The segmentation is performed with the constructed KG for entity designation recognition, candidate entity generation, and candidate entity disambiguation to determine whether it belongs to the business keyword. In a recent study, the Bert model [112] used unlabeled data to vector-code input utterances, vectorize them, and then feed them into a feedforward neural network. The network can automatically determine user intent based on the intent probability distribution. Ref. [113] first pre-trained user input utterances and then achieved an intention recognition accuracy of 92.31% with the pre-training of the Bert model. In addition, the XLNet model [114] has a higher accuracy rate compared to the Bert model. The hybrid neural network rumor detection model based on XLNet [115] achieved an accuracy of 93.56%. The XLNet model is a hot research topic in the field of NPL and a tool that can be adopted in the power market. There are several application scenarios for KG in the field of power customer service. Ref. [113] established an intelligent Q&A architecture based on the knowledge corpus. After recognizing a customer's intention, the expertise is integrated into the dialogue through dialogue process configuration, natural language processing (NLP), and other modules to transform the user's natural language into structured and findable language. Customers can follow the process guidance to complete the demanded business and enjoy intelligent Q&A services. Ref.