## 2 The Framework of the Knowledge Graph  
The concept of a knowledge graph was first introduced by Google in 2012 [3]. Knowledge graphs have been proven to be an effective approach of representing real-world entities, their semantic relationships, and attributes [4]. As a semantic network, knowledge graphs demonstrate powerful expression abilities and modeling interpretability, exhibiting excellent results in knowledge question-answering, knowledge recommendation, knowledge visualization, and other applications [5]. Knowledge in the knowledge graph is presented in the form of triples [6]. There are two fundamental forms: (Entity, Relationship, Entity) and (Entity, Attribute, Attribute-value). As shown in Figure 1, taking the typhoon knowledge graph as an illustration, two distinct knowledge presentation forms are evident. By eliminating the bottleneck of sorting through a large number of data facts and the interconnecting relationships, the knowledge graph allows for skipping extensive searches and narrowing down to the desired solution [7]. This provides the knowledge graph with sufficient power to mimic how human thought. Because of this, knowledge graphs have been successfully applied in many fields such as the Internet, finance, healthcare, and more. To address the issue of un-normalized radio monitoring data, [PERSON] et al. [8] proposed the idea of analyzing massive radio monitoring data based on a knowledge graph. A knowledge base of structured radio monitoring data has been constructed by employing techniques like blind signal recognition. The authors of [9] employ knowledge graph to demonstrate the satellite network topology and routing architecture to optimize the performance of existing routing policy. In [10], an interpretable and efficient decision strategy was obtained with the support of the multi-dimensional knowledge graph. Moreover, a knowledge-graph-assisted collaborative filtering algorithm incorporating path loss was proposed in [11], which constructs a better decision system for satellite-to-ground communication. There are two methods to construct a knowledge graph: the top-down method and the bottom-up method [12]. A comparison of the function of the two construction methods is shown in Table 1. \begin{table}
\begin{tabular}{c c} \hline
**Construction Method** & **Functional Comparison** \\ \hline top-down method & Focuses on definition of knowledge structures and emphasizing the modeling and specification of knowledge. Commonly used in the construction of domain knowledge graphs. \\ bottom-up method & Focuses on automatic extraction of knowledge from various types of data, emphasizing the \\ discovery of knowledge. Commonly used in the construction of open-domain knowledge graphs. \\ \hline \end{tabular}
\end{table} 
Table 1: The comparison of the function of the two construction methods. Figure 1: Typhoon knowledge graph. Spectrum knowledge graphs belong to domain knowledge graphs [13]. Compared to the current static knowledge graphs in the fields of medicine and finance, the high dynamics of satellite platforms, and real-time changes in frequency-usage behavior fundamentally affect the construction of the knowledge graph. For this reason, the schema-layer ontology should be designed to define the framework and rules of knowledge graph, which are used for modeling and the specification of knowledge. Furthermore, LEO spectrum sensing field contains enormous amounts of heterogeneous data, and has strict requirements for accuracy. Thus, it is necessary to be able to extract knowledge from spectrum sensing data and satellite domain knowledge for initial data collection and knowledge discovery. Taking this into consideration, it was decided to fuse the top-down and bottom-up methods to design a knowledge graph construction idea which was applicable to the spectrum sensing data of LEO satellite. The specific construction process is shown in Figure 2.  
According to Figure 2, the first step is to obtain data and partition them.