  Citation: Insaurralde, C.C.; Blasch, E.P.; Costa, P.C.G.; Sampigethaya, K. Uncertainty-Driven Ontology for Decision Support System in Air Transport. Electronics 2022, 11, 362. https://doi.org/10.3390/ electronics11030362 Academic Editor: Jose Eugenio Naranjo Received: 7 December 2021 Accepted: 13 January 2022 Published: 25 January 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). electronics Article Uncertainty-Driven Ontology for Decision Support System in Air Transport Carlos C. Insaurralde 1, *, Erik P. Blasch 2, * , Paulo C. G. Costa 3 and Krishna Sampigethaya 4 1 Bristol Robotics Laboratory, Bristol BS16 1QY, UK 2 MOVEJ Analytics, Dayton, OH 45324, USA 3 Department of Systems Engineering and Operations Research, George Mason University, Fairfax, VA 22030, USA; pcosta@gmu.edu 4 Cyber Intelligence and Security Department, Embry-Riddle Aeronautical University, Prescott, AZ 86301, USA; sampiger@erau.edu * Correspondence: carlos.c.insaurralde@gmx.com (C.C.I.); erik.blasch@gmail.com (E.P.B.) Abstract: Recent electronics advances for air transport have increased aircraft density, volume, and frequency in the airspace. These advances come with control requirements for precise navigation, coordinated Air Traffic Management (ATM) or Unmanned aircraft system Traffic Management (UTM), and proactive security. The tight tolerances of aircraft control necessitate management of spatial uncertainty, timeliness precision, and confidence assessment, which have, respectively, variance, relia- bility, and veracity situation awareness and assessment metrics. Meeting such airspace requirements involves the ability to evaluate how those metrics impact ATM/UTM operations, making the complex interrelationships between them a key aspect for coping with the fast worldwide growth of air trans- port. To support such growth, ontologies have been proposed as a promising technology for making such interrelationships explicit, while facilitating communication between avionics devices. This paper investigates the use of ontologies in support of electronic ATM/UTM operations, highlighting the use of Uncertainty Representation and the Reasoning Evaluation Framework (URREF) in realizing the ability for Air Traffic Controllers (ATCs) to semantically communicate with aircraft operators concerning physical airspace coordination. Using Avionics Analytics Ontology (AAO) endowed with the URREF, application examples based on two airspace situations are presented. Example results for northeast coast of Brazil atmospheric volcanic ash as well as for the Eyjafjallajokull volcano eruption show a 65–80% success in providing warnings to ATCs for airspace control. The paper demonstrates that an ontology-based UTM enhances the capability and accuracy of an ATM to suggest rerouting in the presence of remarkably deteriorated weather conditions. Keywords: Bayesian Networks; decision support system; situation awareness; knowledge engineering; avionics analytics 1. Introduction The explosion of potential aerospace platforms, including unmanned aerial vehicles (UAVs), electric vertical take-off and landing (eVTOL) aircraft, and autonomous air parcel delivery (AAPD) networks, requires advanced large-scale processing methods, such as ontological analytics. Recently, many electronics systems have been using formal ontolo- gies to support database analysis, knowledge aggregation, and graphical methods [1]. One prominent example is the use of Decision Support Systems (DSSs) utilizing societal data, sensor measurements, and electrical equipment, as shown for smart applications in the healthcare and electrical markets [2,3]. The integration of smart avionics includes the interaction between electronic equipment supporting Air Traffic Management (ATM), Unmanned Aerial Systems Traffic Management (UTM), and a Human–Machine Interface (HMI). The HMI requires decision support taxonomies from which automation/autonomy scales, trust management techniques, and credibility assessments align with an ontology [4]. Electronics 2022, 11, 362. https://doi.org/10.3390/electronics11030362 https://www.mdpi.com/journal/electronics