automating knowledge capture in the aerospace domain
We present an approach to automating knowledge extraction in the aerospace engineering domain which has had a fundamental impact on the way engineers manage their collective knowledge built with years of experience. Even though obtaining labelled data in this domain is hard due to the high cost of domain experts' time, the application of the machine learning-based technology was successful, yielding results comparable to the state-of-the-art. Moreover, we present a comparison between several machine learning approaches in extracting knowledge from reports about jet engines. We show that the application of a semi-supervised approach does not provide a significant increase in accuracy so as to justify its adoption due to its much higher computational cost, but that the application of a large-scale approach considerably reduces both training and testing time while keeping accuracy comparable to the standard supervised approach, making it a good choice for this class of application scenarios.
knowledge capture aerospace machine learning information extraction