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International Semantic Web Conference

ISWC 2020: The Semantic Web – ISWC 2020 pp 515–530Cite as

Reasoning Engine for Support Maintenance

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 12507)

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.

Keywords

  • Knowledge graphs
  • Graph embedding
  • Reasoning engine

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  • DOI: 10.1007/978-3-030-62466-8_32
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Notes

  1. 1.

    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.

  2. 2.

    http://www-sop.inria.fr/members/Freddy.Lecue/thales/iswc-2020-in-use-PrescriptiveMaintenance.mp4.

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Acknowledgements

The authors would like to thank Dr. Roger Brooks for his support along the duration of the project.

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Authors and Affiliations

  1. Thales Digital Solution, Québec, QC, G1P4P5, Canada

    Rana Farah, Simon Hallé, Jiye Li, Freddy Lécué, Baptiste Abeloos, Dominique Perron, Pierre-Luc Gregoire, Sebastien Laroche, Michel Mercier & Paul Cocaud

  2. Thales, 92098, Paris La défense, France

    Juliette Mattioli

  3. Inria Sophia Antipolis, 06902, Valbonne, France

    Freddy Lécué

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  1. Rana Farah
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Corresponding authors

Correspondence to Rana Farah or Freddy Lécué .

Editor information

Editors and Affiliations

  1. University of Edinburgh, Edinburgh, UK

    Jeff Z. Pan

  2. University of Liverpool, Liverpool, UK

    Valentina Tamma

  3. University of Bari, Bari, Italy

    Claudia d’Amato

  4. University of California, Santa Barbara, Santa Barbara, CA, USA

    Krzysztof Janowicz

  5. California State University, Long Beach, Long Beach, CA, USA

    Bo Fu

  6. Vienna University of Economics and Business, Vienna, Austria

    Axel Polleres

  7. Rensselaer Polytechnic Institute, Troy, NY, USA

    Oshani Seneviratne

  8. Massachusetts Institute of Technology, Cambridge, MA, USA

    Lalana Kagal

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Farah, R. et al. (2020). Reasoning Engine for Support Maintenance. In: , 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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