## 3 Construction of Knowledge Graph for Spectrum Sensing Data from LEO Satellite
### Ontology Construction  
Ontology is a philosophical concept that represents \"objective existence\". The ontology provides the shared comprehension [16] of information structures, and defines the concept of objects in the domain and the properties that can be used to describe them. There are many methods for ontology construction, including IDEF-5 [17], the seven-step method [18], skeleton method [19], TOVE method [20], loop acquisition method [21], methodology method [22], and the nine-step method [23], etc., which have been applied to various fields of ontology construction. In order to be able to efficiently perform on-satellite sensing data analysis, an accurate conceptual and relational model is crucial in constructing a spectrum knowledge graph. Combining the characteristics of the LEO spectrum sensing field, above methods are synthesized and a five-step method was designed to construct ontology layer. The details are as follows. 1. Analyze domain to determine the scope of ontology. The knowledge to be embodied in the field of the LEO satellite includes the system components and basic information of the satellite, such as the satellite number, type, operator, etc., which is used to identify the satellite. Spectrum sensing domain includes perception beams, perception time, signal bandwidth, center frequency, etc. 2. Consider reusing existing ontology. To the best of our knowledge, no ontology model absolutely applicable to the knowledge graph of the LEO satellite spectrum sensing data is found among the existing constructed knowledge graphs. Therefore, the development and design of the ontology model need to be considered from scratch. 3. Obtain knowledge definitions and design the semantic unit. The significant terms in the spectrum sensing of the LEO satellite should be listed, such as the definition of the concepts related to the satellite system composition in the satellite field. During the process of terms display, attention should be paid to the possible ambiguity issues of certain terms to ensure the accurate formation of professional concepts in the relevant field. To facilitate the standardized definition of object classes, relationships, and attributes in the future, a semantic unit is designed for constraints. The semantic unit is expressed formally as \(S=\{H,R,A\}\), and the specific meanings of each element are as follows:  
1. The non-empty set of abstract entities is represented by \(H=\{h_{1},h_{2},\cdots,h_{n}\}\). Element \(h_{i}\) represents the abstract concept type in the ontology layer, such as the LEO satellite class or signal class. 2. \(R=\{r_{1},r_{2},\cdots,r_{n}\}\) stands for the non-empty set of connected edges in the semantic unit. Conceptual classes are linked by the element \(r_{i}\). For example, kind-of indicates the parent-child relationship of the nodes, part-of indicates the relationship between the whole and the part, etc. 3. The non-empty set of abstract attributes is represented by \(A=\{a_{1},a_{2},\cdots,a_{n}\}\). Both the object attributes and data attributes can be represented by element \(a_{i}\). For example, frequency value and bandwidth value are considered as data attributes, while satellite payload and beam characteristics are considered as object attributes. 4. Define concept classes, attributes and relationships. Ontological classes are abstracted from objectively existing objects. According to the inherent attributes of concepts and the constraints of semantic unit, the relationships between concepts and the relationships between concepts and attributes can be fully described. The defined classes and attributes are shown in Figure 3. Three levels of classes are defined in this paper: the first level class is the LEO satellite, the second level class is the beam, and the third level class is the sensing signal. All other concepts are defined as attributes, with the exception of the third level object class. Figure 4 illustrates the defined relationships.