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Knowledge Based Situation Discovery for Avionics Maintenance

Abstract : For knowledge intensive domains, such as Avionics Maintenance, applying automated analysis comes with a major challenge: formalizing complex domain knowledge and conceiving suitable automated algorithms for real world requirements. In this paper, we propose a study on knowledge discovery to assist avionics maintenance via identifying meaningful Description Logic based complex concepts, called situation discovery, that corresponds to crucial scenarios during device repair. We propose an approach to automatic learning of relevant situations hidden in an ontology, in an unsupervised way. Distinct from ontology based concept learning, where a set of instances is given as positive examples of a target concept, the challenge of learning hidden situations consists in discovering significant situations from exponentially many unknown situations. In this paper we formalize the problem and study some related complexity results as well as the algorithms to solve the problem, together with its application to Avionics Maintenance. The approach has been integrated into an enterprise system and achieves the state-of-the-art result in this application. ACM Reference Format:
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Luis Palacios Medinacelli, Yue Ma, Chantal Reynaud, Gaëlle Lortal. Knowledge Based Situation Discovery for Avionics Maintenance. K-CAP '19: Knowledge Capture Conference, Nov 2019, Marina Del Rey CA, United States. pp.155-162, ⟨10.1145/3360901.3364430⟩. ⟨hal-02930192⟩

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