Differences in naming of celestial objects have impeded our ability to develop a common understanding of the heavens. The stars and constellations so familiar to us are known by many names and configurations depending on your culture of origin. The same is true of man-made objects orbiting Earth. Without a common classification of the items and of the relationships between them, the cataloging, tracking, and management of orbiting objects in relation to other objects such as ships and airplanes is nearly impossible.
SAIC's customers in the space domain have an urgent need to better integrate their space object data both technically and conceptually. Although it is costly and time-consuming to manage and integrate exponentially growing data, the task is vitally important for command and control missions. In order to leverage new technologies and support missions, space domain operators require graphical representation and analytical management of space object data. A well-formed knowledge model based on an 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 for space situational awareness 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. For instance, we tell a computer that birds are feathered creatures that fly; now when the computer encounters feathered flying objects, it will associate that unknown object with a bird. This allows artificial intelligence to assist us 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. Analysts can access and reason with the more diverse data in much more insightful ways. AI intelligent agents can support applications such as object resolution and integration of data across disparate domains.
Think about this concept in relation to sheet music. The notes on sheet music are universal. Regardless of nationality, instrument, player, and time of day, a Middle C is always a Middle C. Because everyone is operating from a common understanding of a Middle C quarter note, we can construct a master score that weaves together all the instruments and use that to teach an artificial intelligence how to operate within the construct. Is your percussionist sick and you need to have a synthetic drummer sub in? That’s possible thanks to a musical ontology.