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Towards Generalized Welding Ontology in Line with ISO and Knowledge Graph Construction

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The Semantic Web: ESWC 2022 Satellite Events (ESWC 2022)

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Abstract

Motivation. Industry 4.0 [1, 2] comes with unprecedented amounts of heterogeneous industrial data [3,4,5].

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Notes

  1. 1.

    http://industryportal.enit.fr/ontologies/RGOM.

  2. 2.

    http://www.loa.istc.cnr.it/dolce/overview.html.

  3. 3.

    https://www.w3.org/TR/owl-time/.

References

  1. Yahya, M., Breslin, J.G., Ali, M.I.: Semantic web and knowledge graphs for industry 4.0. Appl. Sci. 11(11), 5110 (2021)

    Article  Google Scholar 

  2. Sudharsan, B., et al.: Tinyml benchmark: executing fully connected neural networks on commodity microcontrollers. In: 2021 IEEE 7th World Forum on Internet of Things (WF-IoT), pp. 883–884. IEEE (2021)

    Google Scholar 

  3. Zhou, B., Chioua, M., Bauer, M., Schlake, J.-C., Thornhill, N.F.: Improving root cause analysis by detecting and removing transient changes in oscillatory time series with application to a 1, 3-butadiene process. Ind. Eng. Chem. Res. 58, 11234–11250 (2019)

    Article  Google Scholar 

  4. Zhou, B., Chioua, M., Schlake, J.-C.: Practical methods for detecting and removing transient changes in univariate oscillatory time series. IFAC-PapersOnLine 50(1), 7987–7992 (2017)

    Article  Google Scholar 

  5. Chand, S., Davis, J.: What is smart manufacturing. Time Mag. Wrapper 7, 28–33 (2010)

    Google Scholar 

  6. Mikhaylov, D., Zhou, B., Kiedrowski, T., Mikut, R., Lasagni, A.-F.: High accuracy beam splitting using slm combined with ML algorithms. Opt. Lasers Eng. 121, 227–235 (2019)

    Article  Google Scholar 

  7. Mikhaylov, D., Zhou, B., Kiedrowski, T., Mikut, R., Lasagni, A.F.: Machine learning aided phase retrieval algorithm for beam splitting with an LCoS-SLM. In: Laser Resonators, Microresonators, and Beam Control XXI, vol. 10904, International Society for Optics and Photonics, p. 109041M (2019)

    Google Scholar 

  8. Zhou, B., Pychynski, T., Reischl, M., Kharlamov, E., Mikut, R.: Machine learning with domain knowledge for predictive quality monitoring in resistance spot welding. J. Intell. Manuf. 33, 1–25 (2022). https://doi.org/10.1007/s10845-021-01892-y

    Article  Google Scholar 

  9. Zhou, D., et al.: Enhancing knowledge graph generation with ontology reshaping - Bosch case, in: ESWC (Demos/Industry). Springer (2022)

    Google Scholar 

  10. Mailis, T., Kotidis, Y., Christoforidis, S., Kharlamov, E., Ioannidis, Y.E.: View selection over knowledge graphs in triple stores. Proc. VLDB Endow. 14(13), 3281–3294 (2021)

    Article  Google Scholar 

  11. Zhou, B., Zhou, D., Chen, J., Svetashova, Y., Cheng, G., Kharlamov, E.: Scaling usability of ML analytics with knowledge graphs: exemplified with a bosch welding case. In: IJCKG, pp. 54–63. ACM (2021)

    Google Scholar 

  12. Svetashova, Y., et al.: Ontology-enhanced machine learning: a bosch use case of welding quality monitoring. In: Pan, J.Z. (ed.) ISWC 2020. LNCS, vol. 12507, pp. 531–550. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-62466-8_33

    Chapter  Google Scholar 

  13. Zhou, B., Svetashova, Y., Pychynski, T., Kharlamov, E.: Semantic ML For manufacturing monitoring at Bosch. In: ISWC (Demos/Industry), vol. 2721, p. 398 (2020)

    Google Scholar 

  14. Svetashova, Y., Zhou, B., Schmid, S., Pychynski, T., Kharlamov, E.: SemML: Reusable ML for condition monitoring in discrete manufacturing. In: ISWC (Demos/Industry), vol. 2721, pp. 213–218 (2020)

    Google Scholar 

  15. Ho, V.T., Stepanova, D., Gad-Elrab, M.H., Kharlamov, E., Weikum, G.: Learning rules from incomplete KGs using embeddings. In: ISWC Posters & Demos, vol. 2180, CEUR-WS.org (2018)

    Google Scholar 

  16. Zhou, B.: Machine learning methods for product quality monitoring in electric resistance welding, Ph.D. thesis, Karlsruhe Institute of Technology, Germany (2021)

    Google Scholar 

  17. Zhou, B., Pychynski, T., Reischl, M., Mikut, R.: Comparison of machine learning approaches for time-series-based quality monitoring of resistance spot welding (RSW). Arch. Data Sci. Series A (Online First) 5(1), 13 (2018)

    Google Scholar 

  18. Zhou, B., Svetashova, Y., Byeon, S., Pychynski, T., Mikut, R., Kharlamov, E.: Predicting quality of automated welding with machine learning and semantics: a Bosch case study. In: CIKM. ACM, pp. 2933–2940 (2020)

    Google Scholar 

  19. Chang, X., Rai, R., Terpenny, J.: Design for manufacturing (DFM) ontology: implementation of a mechanical assembly through welding process. In: IIE Annual Conference. Proceedings, p. 1690 (2007)

    Google Scholar 

  20. Saha, S., Usman, Z., Li, W., Jones, S., Shah, N.: Core domain ontology for joining processes to consolidate welding standards. Robot. Comput.-Integr. Manuf. 59, 417–430 (2019)

    Article  Google Scholar 

  21. Zhou, B., Svetashova, Y., Pychynski, T., Baimuratov, I., Soylu, A., Kharlamov, E.: SemFE: facilitating ML pipeline development with semantics. In: CIKM. ACM, pp. 3489–3492 (2020)

    Google Scholar 

  22. Zhou, B., et al.: SemML: Facilitating development of ML models for condition monitoring with semantics. J. Web Semant. 71, 100664 (2021)

    Article  Google Scholar 

  23. Zhou, B., et al.: The data value quest: a holistic semantic approach at Bosch. In: ESWC (Demos/Industry). Springer (2022)

    Google Scholar 

  24. Zhou, D., Zhou, B., Chen, J., Cheng, G., Kostylev, E., Kharlamov, E.: Towards ontology reshaping for KG generation with user-in-the-loop: applied to Bosch welding. In: IJCKG. ACM, pp. 145–150 (2021)

    Google Scholar 

  25. Zheng, Z., et al.: Query-based industrial analytics over knowledge graphs with ontology reshaping. In: ESWC (Posters & Demos). Springer (2022)

    Google Scholar 

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Acknowledgements

This work was partially supported by SFI (Grant 16/RC/ 3918), and the H2020 projects Dome 4.0 (Grant Agreement No. 953163), OntoCommons (Grant Agreement No. 958371), and DataCloud (Grant Agreement No. 101016835) and the SIRIUS Centre, Norwegian Research Council project number 237898. For the purpose of Open Access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.

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Authors and Affiliations

  1. Confirm Smart Manufacturing, National University of Ireland, Galway, Ireland

    Muhammad Yahya & John G. Breslin

  2. Insight Centre for Data Analytics, National University of Ireland, Galway, Ireland

    Muhammad Yahya & John G. Breslin

  3. SIRIUS Centre, University of Oslo, Oslo, Norway

    Baifan Zhou, Dongzhuoran Zhou & Evgeny Kharlamov

  4. Bosch Center for Artificial Intelligence, Renningen, Germany

    Zhuoxun Zheng, Dongzhuoran Zhou & Evgeny Kharlamov

  5. Department of Computer Science, Oslo Metropolitan University, Oslo, Norway

    Zhuoxun Zheng

  6. School of Electronic Engineering, Dublin City University, Dublin, Ireland

    Muhammad Intizar Ali

Authors
  1. Muhammad Yahya
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  2. Baifan Zhou
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  3. Zhuoxun Zheng
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  4. Dongzhuoran Zhou
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  5. John G. Breslin
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  6. Muhammad Intizar Ali
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  7. Evgeny Kharlamov
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Corresponding author

Correspondence to Muhammad Yahya .

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Editors and Affiliations

  1. Faculty of Science, Informatics Institute, University of Amsterdam, Amsterdam, Noord-Holland, The Netherlands

    Paul Groth

  2. Department of Computer Engineering, University of Brescia, Brescia, Italy

    Anisa Rula

  3. School of Information Sciences, University of Illinois Urbana-Champaign, Champaign, IL, USA

    Jodi Schneider

  4. Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

    Ilaria Tiddi

  5. Bush House, Strand Campus, King’s College London, London, UK

    Elena Simperl

  6. Textkernel BV, Amsterdam, The Netherlands

    Panos Alexopoulos

  7. Elsevier BV, Amsterdam, The Netherlands

    Rinke Hoekstra

  8. FIZ Karlsruhe - Leibniz Institute for Information Infrastructure, Eggenstein-Leopoldshafen, Germany

    Mehwish Alam

  9. Department of Computer Science, KU Leuven, Sint-Katelijne-Waver, Belgium

    Anastasia Dimou

  10. Department of Computer Science, Aalto University, Espoo, Finland

    Minna Tamper

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Yahya, M. et al. (2022). Towards Generalized Welding Ontology in Line with ISO and Knowledge Graph Construction. In: Groth, P., et al. The Semantic Web: ESWC 2022 Satellite Events. ESWC 2022. Lecture Notes in Computer Science, vol 13384. Springer, Cham. https://doi.org/10.1007/978-3-031-11609-4_16

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