## 2 The Framework of the Knowledge Graph
There are three types of data structure, structured data, semi-structured data, and unstructured data. Structured data are data that have a fixed format and structure, such as relational database data. Semi structured data are data that have a certain hierarchical structure but are different from traditional structured data forms, such as JSON. Unstructured data refers to data without a clear structure, such as text, images, or video. By observing the data obtained, spectrum data and ephemeris data belong to semi-structured data, while satellite platform and payload information belong to unstructured data. Knowledge extraction is then performed. Knowledge extraction is the process of extracting entities, relationships and attributes from raw data. Natural language processing and information extraction techniques are often involved. There are many methods of knowledge extraction in practical applications. Depending on the development history, they are mainly categorized into rule- and dictionary-based methods, machine-learning-based methods, and deep-learning-based methods. Unstructured or semi-structured data are converted into structured knowledge through this process. The underlying knowledge structure is built by identifying entities, relationships and attributes. Subsequently, the ontology layer of the spectrum knowledge graph is designed from LEO satellite spectrum sensing perspective to constraint the framework and boundary of knowledge. The ontology provides a common understanding of the information structure and formalized knowledge structures, and clarifies the hierarchical structure between entities. It can be used to define concepts of things in the domain and properties that can be used to describe them. However, the method of  
Figure 2: The specific constructing process of spectrum knowledge graph. ontology construction is not standardized due to the different domains and purposes of construction. The following methods are commonly recognized in the field: Skeleton Method, Loop Acquisition Method, Methodology Method, Seven-Step Method, and so on. Due to the knowledge extraction of data with different structural types, problems such as knowledge redundancy and knowledge conflict are inevitable. These problems can impact the quality of the extracted knowledge, which in turn affects the results of the data to be analyzed and mined. Therefore, knowledge fusion is necessary to be executed for the extracted knowledge. Knowledge fusion is usually achieved through two steps: entity linking and knowledge mapping. The problems of data redundancy and data conflict can be effectively solved through knowledge fusion, and the extracted data can be consistently integrated under the constraints of the ontology. Finally, the triples constrained by the ontology layer are transformed into nodes and links, and stored in spectrum knowledge graph. Knowledge visualization refers to presenting knowledge graphs in a graphical manner. This process is usually implemented with the help of graph visualization tools such as Neo4j, Apache Jena, etc. Through graphical presentation, the fused knowledge can be explicitly observed, which leads to a better understanding and mastery of related information.