Abstract
The problem of repair and maintenance of complex systems, such as aircraft, cars and trucks is a nontrivial task. Maintenance technicians must use a great amount of knowledge and information resources to solve problems that may occur. This paper describes a semi-automated tool that sorts through the mass of information that a maintenance technician must consult in order to make a repair, thus helping him decide how to tackle the problem and thereby increasing his efficiency and, possibly, his reliability. Our tool was developed using state-of-the-art Case-Based Reasoning and Information Extraction technologies. More specifically, we developed a semi-automated Case Authoring method that creates a Case-Base in two steps. It begins by extracting knowledge from readily available resources such as technical documents and follows by complementing those cases using individual experiences in the maintenance organization. The case-base developed is a reflection of the knowledge encoded in the technical documentation and an authentication of the cases with real historical instances. Our case authoring approach is applied to the real world in the aerospace domain.
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Zaluski, M., Japkowicz, N., Matwin, S. (2003). Case Authoring from Text and Historical Experiences. In: Xiang, Y., Chaib-draa, B. (eds) Advances in Artificial Intelligence. Canadian AI 2003. Lecture Notes in Computer Science, vol 2671. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44886-1_18
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DOI: https://doi.org/10.1007/3-540-44886-1_18
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