With the growing congestion in the airspace, Air Traffic Management (ATM) requires advances in massive data processing, sophisticated avionics techniques, coordination with weather updates, and assessment of multiple types of uncertainty. The complex situation overwhelms pilots and ATM controllers. To provide dependable artificial decision-making support for ATM and Unmanned Aerial System Traffic Management (UTM) systems, ontologies are an attractive knowledge technology. This paper proposes an Avionics Analytics Ontology (AAO) to bring together different types of uncertainties including semantic from operators, sensing from navigation, and situation from weather modeling updates. The approach is aligned with the Uncertainty Representation and Reasoning Evaluation Framework (URREF), that develops an uncertainty ontology. The degree of uncertainty to improve effectiveness in ATM/UTM decision-making processes quantifies information veracity; in addition to accuracy, timeliness, and confidence. Application examples are presented that involves two ATM/UTM operation scenarios where Unmanned Aerial Vehicles (UAVs) fly nearby commercial aircraft and/or airports which requires situation awareness safety response. As compared to a baseline approach without Automatic Dependent Surveillance-Broadcast (ADS-B), results from recorded ADS-B data demonstrate a over 0.75 veracity improvement) from Newark Liberty International Airport.