Uncertainty Considerations for Ontological Decision-Making Support in Avionics Analytics

The Federal Aviation Administration (FAA) and the European Aviation Safety Agency (EASA) have an emerging interest in ontologies. A common ontology for avionics analytics can help pilots and Air Traffic Controllers (ATCs) make difficult decisions with increasingly-sophisticated avionics, densely-occupied airspaces, and progressively-adverse weather. The connected airspace sets a context of big data, enriched features, and information uncertainty. This paper proposes to endow an Avionics Analytics Ontology (AAO) with semantic uncertainty to improve decision-making capabilities. The proposed approach incorporates the Uncertainty Representation and Reasoning Evaluation Framework (URREF) into the AAO for input information. The AAO focuses on veracity as a key component of information credibility to deal with uncertainty. The URREF assessment aims to enhance avionics analytics when considering semantic and physical data sources. Thus, situation AWareness (SAW) and Situation Assessment (SA) as well as Situation Understanding (SU) in information fusion are ultimately enhanced by means of statistical metrics of veracity. This paper also shows experimental results from two application scenarios. Concluding remarks and future research directions are also presented.