## 3 Construction of Knowledge Graph for Spectrum Sensing Data from LEO Satellite
### Knowledge Fusion and Knowledge Storage  
When extracting knowledge from data with different structure types, problems such as knowledge redundancy and knowledge conflicts inevitably arise. These problems impair the quality of the extracted knowledge, which in turn affects the results of the data to be analyzed and utilized. After knowledge extraction, the data are fused through entity linking and knowledge mapping for knowledge fusion. The entity linking process primarily employs a programming language to generate unique flags for the information extracted under each specific time, space, and frequency band, and then categorizes and packages them. Entities and attributes with associations in different data types are linked, and duplicate entities are deleted. The knowledge mapping process marks each entity knowledge, relationship, and attribute knowledge, respectively, by the constraint of conceptual class, relationship, and attribute in the ontology layer. After knowledge mapping, the data in CSV files are mapped into the form of the semantic unit in the ontology, eventually forming triples. The common types of spectrum knowledge graph triples are shown in Table 2. The purpose of knowledge fusion is to eliminate invalid knowledge, avoid information silos, and make knowledge more connected and valuable.  
After knowledge fusion, the spectrum knowledge graph is stored and visualized through graphical means. Neo4j [25] graphical database was chosen for the visualization display. The spectrum knowledge graph was stored and visualized in the graph database of Neo4j by importing the py2 neo module. Neo4j is a widely used graph database, which provides a flexible and interpretable platform for visualizing and querying large-scale data [26]. In Neo4j, fine-grained queries about entities and relationships can be performed  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Head Entity** & **Relationship** & **Tail Entity/Attribute** \\ \hline LEO Satellite & be\_provided\_with & Beam \\ LEO Satellite & Consists\_of & Satellite Payload/Satellite Platform \\ Beam & Sensing\_of & Sensing Signals \\ Sensing Signal & Value\_is & Bandwidth Value/Frequency Value, etc. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Spectrum knowledge graph triples.  
Figure 5: The integral schema-layer ontology. Please note that the dashed lines represent the relationship defined in Figure 4. Different colored dashed lines represent different relationships.  
by using the Cypher language and Match language. The entities and relationships associated with query knowledge can be presented together. The visualization sample of spectrum the knowledge graph is shown in Figure 6. Figure 7 shows the local detail of spectrum knowledge graph. Compared with the original data, it can be considered that the spectrum knowledge graph construction method proposed in this paper is complete and feasible. Based on the semantic unit, the constructed spectrum knowledge graph logically forms a vertical associative relationship with ontology layer, data layer, and cross-mapping relationship layer, but forms a horizontal networked topological structure with the corresponding concepts, attributes, and relationships.  
Figure 6: Visualization of knowledge graph of LEO satellite spectrum sensing data.