## 5 Summary and Outlook  
In order to efficiently perform on-satellite spectrum sensing data analysis and spectrum posture generation, a knowledge graph construction and semantic representation idea for LEO satellite spectrum sensing data is designed in this paper. By efficiently rearranging LEO satellite spectrum sensing data, data integration at the semantic level can be accomplished. As a result, the intricate and dynamic environment of the frequency-usage posture can be better depicted. However, a truly usable spectrum knowledge graph relies on the long-term construction of multiple satellites and worldwide spectrum sensing data. In this context, the collaborative updating and knowledge fusion of the LEO satellite spectrum knowledge graphs are particularly crucial. 1. Scalable ontology model: With the increase of frequency-usage devices, the types of frequency-usage devices are gradually diversified. This makes the sensing information characteristics also diversify. The diversity will pose a challenge to the construction of ontology model. If the classes, relations and attributes defined in the ontology model are too homogeneous, the diversity of sensing features cannot be adequately characterized, and the validity of the results of the analysis and prediction of frequency-usage behaviors will also be reduced. Further research on data-driven scalable ontology construction methods is necessary. The ontology does not need to be reconstructed when the complexity of the sensing information features is enhanced. The original ontology can be evolved with automated or semi-automated extensions to fully characterize the diversity of sensing features. Thus, the validity of the results of frequency-usage behavior analysis and prediction can be improved. 2. Collaborative updating: As the number of frequency-usage devices continues to increase, the status of each device is constantly changing. This will enhance the dynamism of global sensing, such as the increased probability of device switching on/off, switching of frequency-usage patterns, and diversification of behaviors. At the same time, the spectrum environment may also undergo significant changes in a short period of time. Therefore, the spectrum knowledge graph needs to be able to reflect the dynamic changes of the spectrum posture in a timely manner. If the constructed spectrum knowledge graph can be dynamically updated and evolved, it can continuously learn and adapt to the real-time changing frequency environment. By rapidly capturing the complex changes in the frequency-usage posture, the dynamic knowledge graph can furnish a comprehensive cognition of the frequency-usage posture, and offer more diversified and in-depth data support for the cognition of the frequency-usage posture. Owing to the specificity of the field, knowledge in the spectrum domain is often constrained by temporal and spatial factors, such as the high dynamism, time-sharing asynchronous observation of LEO satellites, etc. These problems can be mitigated by adding attributes such as time and geographic location. Nevertheless, it may result in a rise in the quantity of entities and relationships. Therefore, the spectrum knowledge graph is to some extent distinguished from other fields by its short timeliness and frequent updates. The problem of how to efficiently perform dynamic updating of on-satellite knowledge graph has to be considered. 3. Knowledge fusion: With the continuous development of satellite constellations and inter-satellite networking technology, multiple satellites can be interlinked for sensing. By fusing knowledge graphs from different satellites, various frequency-usage behaviors, patterns, and trends can be more comprehensively understood. By doing so, more accurate information can be provided for the rational planning and allocation of spectrum resources to optimize resource scheduling. However, distinct variations in satellite orbits and observation angles result in disparities in the constructed spectrum knowledge graphs on various satellite.