## 2 Related Work  
With the development of knowledge graphs, their applications have received extensive attention. Question answering based on knowledge graph (KBQA) uses structured data as background knowledge to obtain answers to questions. The key to the KBQA is how to provide users with complete answers.  
With the aim of providing users with a complete answer, there are currently two types of methods. As for the first one, the answer is obtained by reasoning about the potential knowledge in the knowledge graph. For example, the question is parsed and converted into a query language to obtain the answer [9; 10; 11; 12; 13; 14]. The knowledge in the question is extracted and mapped to the knowledge graph and candidate answers are selected and pruned to determine the final answer [15; 16; 17]. In addition, machine learning methods are used for reasoning. The learning ability of the machine learning model allows one to dig out connections between knowledge in a large amount of training data to obtain answers [18; 19; 20; 21; 22].  
The other type is to use the method of entity alignment to expand the scope of knowledge graphs to answer questions. For example, [PERSON] et al. [23; 24] used text knowledge for the KBQA as additional background knowledge. [PERSON] et al. [25] designed a model that used knowledge graphs to explain VQA to verify the answer. To improve VQA, [PERSON] et al. [6] put forward a video understanding model, which combined the visual information, text information of the videos, and the knowledge in the knowledge graph. Besides, in order to use a knowledge graph to solve problems in the intersection of two specific domains, some researchers proposed methods to extend a knowledge graph. After the expansion, a large-scale knowledge graph was obtained and then cross-domain questions can be answered using the knowledge graph-based question answering method, such as in the literature [26; 27]. With the development of knowledge graphs and question answering methods, there have also been some methods discussing how to make the linked knowledge graph expand dynamically. For example, in the literature [28], with the dynamic increase of knowledge graph knowledge, the entire background knowledge base is dynamically updated. Although the first method can infer some potential knowledge in the knowledge graph, it still cannot answer questions that involve both domain knowledge and common-sense knowledge. Although the second method makes up for the shortcomings of the first method, it adds the steps of entity alignment and increases the difficulty of questioning and answering.  
The first approach obtains latent knowledge in the knowledge graph through reasoning, and the second approach adds background knowledge through physics. This paper refers to the second method to increase structured knowledge, expand the background knowledge base, and use two knowledge graphs to answer questions instead of complex operations such as entity alignment. Considering the complexity of using the entity alignment method, a preferential labeling method is designed to use these two different knowledge graphs to achieve the goal of obtaining a complete answer.