Applying Semantic Web Technologies to Assess Maintenance Tasks from Operational Interruptions: A Use-Case at Airbus

摘要

Airbus, one of the leading Aircraft company in Europe, collects and manages a substantial amount of unstructured data from airlines companies, related to events occurring during the exploitation of an aircraft. Those events are called 'Operational Interruptions' (OI) describing observations and the work performed associated by operators in form of short text. At the same time, Airbus maintains a dataset of programmed maintenance task (MPD) for each family of aircraft. Currently, OIs are reported by companies in Excel spreadsheets and experts have to find manually in the OIs the ones that are most likely to match an existing task. In this paper, we describe a semi-automatic approach using semantic technologies to assist the experts of the domain to improve the matching process of OIs with related MPD. Our approach combines text annotation using GATE and a graph matching algorithm. The evaluation of the approach shows the benefits of using semantic technologies to manage unstructured data and future applications for data integration at Airbus.