Semantic Knowledge Graph (sKG) to Apply Generative AI to Air Force Data

A common challenge facing the Air Force, and the DoD as a whole, whether with regards to strategic or tactical level data platforms, is that the current solutions lack the ability to deliver a sustainable and scalable method of structuring the data in order for the data to be explorable & interrogated and for machine learning, artificial intelligence capabilities to be applied to the data in a meaningful manner. Many systems, data lakes or data platforms conform to proprietary formats, focus solely on visualizing analytics versus data organization and structure, which create current and future challenges for the Air Force and the Department of Defense. These challenges include the lack of consistency in the data, disorganized data and disparate data which is too cumbersome. This continually results in a stovepiped and disconnected systems while the Air Force persistently searches for solutions which need to scale and support current and future operations. While the Air Force, the DoD and the defense industrial base have attempted to solve the aforementioned set of problems, they have done so leveraging various types of user interfaces focusing primarily on the visualization vice the data behind the visualization. Companies continue to fail at delivering a solution because they don’t focus on the core of the problem: data organization, data engineering and accessibility with an analogous structure which can be integrated with any data analytics or visualization solutions. Currently, organizations will push their data into a data lake or data cloud in an effort to have the data ‘centrally’ located in order to develop ways to query and investigate their workloads. The challenge, when organizations blindly integrate the data into large repositories, the ability to discover, make sense of and conduct analysis of that data can be cumbersome and daunting.