Abstract
This paper presents a reasoning system deployed for supporting the maintenance of IT devices in use by a leading broadcasting and cable television company in North America. We describe a reasoning engine pipeline relying on semantic data representation and some machine learning approaches such as clustering. The engine derives problems on a telecommunication network from a textual description and uses structured historical data of problems, error codes and proposed solutions to prescribe potential solutions. The engine is capable of proposing solutions to unseen problems by using analogical reasoning on structured representations. When a problem happens on the network or more precisely on one of the devices, these devices generate error codes. We addressed two scenarios; (i) we assumed that the list of error codes that we captured is complete, (ii) we assumed, more realistically, that this list is incomplete. In the first case, we suggested solutions for seen and new problems and reported results on real data. In the second case, we proposed a method to infer the complete list of errors, tested that method on synthetic data and showed results with high accuracy. Although both scenarios are in-use, the first scenario is more usual than the second one, but both need to be considered.
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Notes
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The details and results of the embedding algorithms comparison are at http://www-sop.inria.fr/members/Freddy.Lecue/thales/iswc-2020-in-use-PrescriptiveMaintenance-extra-results.pdf.
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The authors would like to thank Dr. Roger Brooks for his support along the duration of the project.
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Farah, R. et al. (2020). Reasoning Engine for Support Maintenance. In: Pan, J.Z., et al. The Semantic Web – ISWC 2020. ISWC 2020. Lecture Notes in Computer Science(), vol 12507. Springer, Cham. https://doi.org/10.1007/978-3-030-62466-8_32
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DOI: https://doi.org/10.1007/978-3-030-62466-8_32
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