## 1 Introduction  
Space-based sensing has emerged as a crucial application for the development of low earth orbit (LEO) constellations. However, space-based sensing has certain limitations. Firstly, LEO satellites are highly dynamic, and the objects they observe change dynamically in both time and space. Sensing data are massively stacked on LEO satellite [1], and the data are scattered and lack comprehensibility. Secondly, the satellite-to-ground link has limited transmission capacity, and the limited link bandwidth results in slower data transmission speed. As a result, there is a certain transmission delay, especially when transmitting large amounts of data. This delay can negatively impact the real-time performance of the data. Data may lose practical value when they need to be transmitted to the ground station or elsewhere for processing. Additionally, due to the long propagation distance between satellite and ground [2], the receiving sensitivity is limited and spatial resolution is low. This makes it difficult to achieve accurate sensing even if the satellite can carry a larger antenna. To fully utilize the semantic and scalable performance of sensing data, real-time and efficient means of massive data mining and analysis processing are required. Semantic extraction has been proposed as the main solution by existing researches. However, most studies are carried out on verbal texts, images or videos. Research on space-based spectrum sensing data is still pending. Especially, existing semantic extraction is dominated by deep neural network approaches. There are issues with subjective codebook design, invisible extraction and recovery processes, and weak physical interpretability of extraction results. In recent years, knowledge graph technology has received widespread attention. It forms a relational semantic web of the concepts, entities and relationships between them in the objective world, in a form more akin to the human cognition. As a matter of fact, frequency-usage posture is actually a discrete sampling of the electromagnetic spatial relations of continuous time, space, frequency and energy. This physical context of relationalization makes it suitable for description in terms of relational networks. A knowledge graph, as a relational descriptive model of structured information, has the ability to handle large amounts of data. Precisely because of this, the knowledge graph can be used as a way to characterize the frequency-usage posture. Spectrum posture can be effectively modeled by knowledge graph at the semantic level. With the knowledge graph, the data are correlated in the time, space and frequency, and the concepts and inter-conceptual relationships in the spectrum-sensing data are captured at the semantic level. The spatiotemporal distribution and dynamic characteristics of frequency-usage behavior can be more deeply understood. As well as, the amount of data are reduced to a certain extent, which helps to ease the burden of satellite-to-ground transmission and dilute the transmission delay. It is worth mentioning that the knowledge graph has the capability of knowledge reasoning. 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. Accordingly, the reasoning ability of the knowledge graph can be utilized to realize the analysis and prediction of frequency-usage behavior. On-satellite spectrum sensing data are not only semantically integrated by the relational network, but can be fully utilized to realize the sensing value. Therefore, the construction of a knowledge graph in the field of LEO satellite spectrum sensing is investigated in this paper. There are two methods of knowledge graph construction: bottom-up approach and top-down approach.