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Ontology-Enhanced Machine Learning: A Bosch Use Case of Welding Quality Monitoring

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The Semantic Web – ISWC 2020 (ISWC 2020)

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

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Abstract

In the automotive industry, welding is a critical process of automated manufacturing and its quality monitoring is important. IoT technologies behind automated factories enable adoption of Machine Learning (ML) approaches for quality monitoring. Development of such ML models requires collaborative work of experts from different areas, including data scientists, engineers, process experts, and managers. The asymmetry of their backgrounds, the high variety and diversity of data relevant for quality monitoring pose significant challenges for ML modeling. In this work, we address these challenges by empowering ML-based quality monitoring methods with semantic technologies. We propose a system, called SemML, for ontology-enhanced ML pipeline development. It has several novel components and relies on ontologies and ontology templates for task negotiation and for data and ML feature annotation. We evaluated SemML on the Bosch use-case of electric resistance welding with very promising results.

Y. Svetashova and B. Zhou—Contributed equally to this work as first authors.

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Author information

Authors and Affiliations

  1. Bosch Corporate Research, Renningen, Germany

    Yulia Svetashova, Baifan Zhou, Tim Pychynski & Stefan Schmidt

  2. Karlsruhe Institute of Technology, Karlsruhe, Germany

    Yulia Svetashova, Baifan Zhou, York Sure-Vetter & Ralf Mikut

  3. Bosch Center for Artificial Intelligence, Renningen, Germany

    Evgeny Kharlamov

  4. University of Oslo, Oslo, Norway

    Evgeny Kharlamov

Authors
  1. Yulia Svetashova
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  2. Baifan Zhou
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  3. Tim Pychynski
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  4. Stefan Schmidt
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  5. York Sure-Vetter
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  6. Ralf Mikut
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  7. Evgeny Kharlamov
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Corresponding authors

Correspondence to Yulia Svetashova or Baifan Zhou .

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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Svetashova, Y. et al. (2020). Ontology-Enhanced Machine Learning: A Bosch Use Case of Welding Quality Monitoring. 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_33

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  • DOI: https://doi.org/10.1007/978-3-030-62466-8_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-62465-1

  • Online ISBN: 978-3-030-62466-8

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