EBTOnto is an OWL DL ontology that allows to formalize knowledge about airline pilots training from real cases, laying the foundations for a semantic knowledge base of scenarios for this domain.
The ontology is built on top of a source validated by experts, the Evidence-Based Training (EBT) Implementation Guide by the International Air Transport Association (IATA), and then checked using an automatic reasoner and a database of 37,568 aviation safety incidents, extracted from the widely regarded Aviation Safety Reporting System (ASRS) by the U.S. National Aeronautics and Space Administration.
Our research suggest that it is possible to classify real aviation scenarios in terms of non-technical competencies and filter useful incident reports for design and enrichment of these training scenarios. EBTOnto opens up new possibilities for interoperability between incident databases and training organizations, and smoothes the path to represent, share and generate custom simulation training scenarios for pilots based on real data.