The process of building the upper-level hierarchy for the aircraft structure ontology to be integrated in FunGramKB

In this article we collect a corpus of texts which operate with a controlled language (ASD Simplified Technical English) in order to facilitate the development of a new domain-specific ontology (the aircraft structure) based on a technical discipline (aeronautical engineering) included in the so called “hard” sciences. This new repository should be compatible with the Core Ontology and the corresponding English Lexicon in FunGramKB (a multipurpose lexico-conceptual knowledge base for natural language processing (NLP)), and, in the same vein, should eventually give support to aircraft maintenance management systems. By contrast, in previous approaches we applied a stepwise methodology for the construction of a domain-specific subontology compatible with FunGramKB systems in criminal law, but the high occurrence of terminological banalisation and the scarce number of specific terms, due to the social nature of the discipline, were added problems to the most common NLP difficulties (polysemy and ambiguity). Taking into consideration previous results and the complexity of this task, here we only intend to take the first step towards the modelling of the aircraft ontology: the development of its taxonomic hierarchy. Consequently, the hierarchy starts with the whole system (i.e., an aircraft) and follows the traditional decomposition of the system down to the elementary components (top-down approach). At the same time, we have collected a corpus of 2,480 files of aircraft maintenance instructions, courtesy of Airbus in Seville. For the bottom-up approach (under construction), we consult specialised references end explore the corpus through the identification and extraction of term candidates with DEXTER, an online multilingual workbench especially designed for the discovery and extraction of terms

The Proposal of a Concept of Artificial Situational Awareness in ATC

Automation is one of the most promising solutions to the airspace capacity problem. However, we believe that in order to safely implement advanced automation concepts in air traffic control, it is necessary for AI and humans to share situational awareness. One of the main objectives of this concept proposal is to explore the effects and possi-bilities of distributed human-machine situational awareness in en-route air traffic control operations. Instead of automating isolated individual tasks, such as conflict detection or coordination, we propose to create a basis for automation by developing an intelligent situation-aware system. The sharing of the same situational awareness be-tween the members of the air traffic controller team and AI enables the automated system to reach the same conclu-sions as air traffic controllers when faced with the same problem and to be able to explain the reasons for these conclusions. Machine learning can be used to predict, estimate and filter at the level of individual probabilistic events, an area in which it has shown great ability so far, whereas the reasoning engine can be used to represent knowledge and draw conclusions based on all the available data and explain the reasons for these conclusions. In this way, the artificial situational awareness system will pave the way for future advanced automation based on machine learning. Here, we will explore which technologies and concepts are useful in building the artificial situational awareness system and propose the methodology for testing the AI situational awareness