Managing and integrating exponentially growing space data is important for developing the space situational awareness needed for command and control missions. That is why a well-formed knowledge model based on open-standard ontology could serve as the cornerstone of effective space situational awareness while contributing to all-domain command and control.
Managing and integrating exponentially growing space data is important for developing the space situational awareness needed for command and control missions. That is why a well-formed knowledge model based on open-standard ontology could serve as the cornerstone of effective space situational awareness while contributing to all-domain command and control.
The process of developing a well-formed knowledge model starts by identifying and clearly defining the entities, entity attributes, and processes. These entities make up the class terms that are placed into taxonomical hierarchies, along with logical relationships that facilitate reasoning with the entities, i.e., ontologies.
Ontologies provide relevant, formal specifications of concepts and relationships to entities, and, therefore, the data is linked to its meaning. This allows Artificial Intelligence (AI) to assist in generating integrated knowledge graphs using instance data or to link to existing data across disparate sources, resulting in the integration of large data sets based upon common semantics. The resulting framework can create critical benefits. For instance, analysts can access and reason with the more diverse data in more insightful ways. In addition, AI intelligent agents can support applications such as object resolution and integration of data across disparate domains.
In practice, leveraging space object ontology is challenging because there is confusion in the data terms and standards that make the rapid formulation of the common knowledge model difficult. Therefore, to widely implement ontology-based knowledge models to solve real-world data management and analysis problems, careful consideration of ontology’s structure, available data, and the target technology stack is required.
Consequently, there is a need to explore novel ways to take advantage of hybrid database configuration and Artificial Intelligence (AI) to transform unstructured data into structured graphs. Furthermore, investments are needed for developing ontologies for data fusion and reasoning via data analytics.
With both the population of objects in Earth's orbit and complexity of the space domain growing, it is important to organize and manage space object data to ensure and maintain a safe and sustainable orbital environment. Developing an ontology-based knowledge model will help to promote communication and a common understanding of the entities in the space domain.
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