Transforming unstructured, original documentation into structured digital knowledge requires specialized ontological modeling and Natural Language Processing. By parsing regulatory content, maintenance logs, and flight records into machine-readable structures, aviation organizations can build robust digital threads to support predictive modeling, compliance, and safe aircraft operations. [1, 2, 3, 4, 5]

Structuring aviation knowledge typically involves several core processes: [1]

For many organizations, the goal is to bridge the gap between human-readable data (like PDF manuals or NOTAMs) and digital modeling. For instance, platforms are exploring generative models that reconstruct operational patterns and approach trajectories from raw data without manual labeling. [1, 2, 3]

Original aviation data usually comes in a few different formats:

Imagine an original source says: "Inspect the landing gear every 100 flight hours."

You can turn that single sentence into structured digital code like this: