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Enhancing Knowledge Graph Generation with Ontology Reshaping – Bosch Case

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

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Abstract

In the context of Industry 4.0 [1] and Internet of Things (IoT) [2], modern manufacturing and production [3, 4] lines are equipped with software systems and sensors that constantly collect and send a high volume of data.

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References

  1. Kagermann, H.: Change through digitization—value creation in the age of Industry 4.0. In: Albach, H., Meffert, H., Pinkwart, A., Reichwald, R. (eds.) Management of Permanent Change, pp. 23–45. Springer, Wiesbaden (2015). https://doi.org/10.1007/978-3-658-05014-6_2

    Chapter  Google Scholar 

  2. ITU, Recommendation ITU - T Y.2060: overview of the internet of things, Technical report, International Telecommunication Union

    Google Scholar 

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

    Google Scholar 

  4. Wuest, T., Weimer, D., Irgens, C., Thoben, K.-D.: Machine learning in manufacturing: advantages, challenges, and applications. Prod. Manuf. Res. 4, 23–45 (2016)

    Google Scholar 

  5. 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(4), 1139–1163 (2022)

    Article  Google Scholar 

  6. Kharlamov, E., et al.: Ontology based data access in Statoil. J. Web Semant. 44, 3–36 (2017)

    Article  Google Scholar 

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

    Google Scholar 

  8. Zou, X.: A survey on application of knowledge graph. In: Journal of Physics: Conference Series, vol. 1487, p. 012016. IOP Publishing (2020)

    Google Scholar 

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

    Google Scholar 

  10. 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. Ser. A (Online First) 5(1), 13 (2018)

    Google Scholar 

  11. Soylu, A., et al.: TheyBuyForYou platform and knowledge graph: expanding horizons in public procurement with open linked data. Semant. Web 13(2), 265–291 (2022)

    Article  Google Scholar 

  12. 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 

  13. 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 

  14. Horrocks, I., Giese, M., Kharlamov, E., Waaler, A.: Using semantic technology to tame the data variety challenge. IEEE Internet Comput. 20(6), 62–66 (2016)

    Article  Google Scholar 

  15. 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 (2020)

    Google Scholar 

  16. Zhou, B., et al.: Method for resistance welding, US Patent App. 17/199,904 (2021)

    Google Scholar 

  17. Kalaycı, E.G., et al.: Semantic integration of Bosch manufacturing data using virtual knowledge graphs. In: Pan, J.Z., et al. (eds.) ISWC 2020. LNCS, vol. 12507, pp. 464–481. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-62466-8_29

    Chapter  Google Scholar 

  18. Kharlamov, E., et al.: Semantic access to streaming and static data at Siemens. J. Web Semant. 44, 54–74 (2017)

    Article  Google Scholar 

  19. Hubauer, T., Lamparter, S., Haase, P., Herzig, D.M.: Use cases of the industrial knowledge graph at siemens. In: ISWC (P &D/Industry/BlueSky) (2018)

    Google Scholar 

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

    Google Scholar 

  21. Svetashova, Y., et al.: Ontology-enhanced machine learning: a Bosch use case of welding quality monitoring. In: Pan, J.Z., et al. (eds.) 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 

  22. 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 

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

    Google Scholar 

  24. Smith, B.: Ontology. In: The Furniture of the World, pp. 47–68. Brill (2012)

    Google Scholar 

  25. Guarino, N., Oberle, D., Staab, S.: What is an ontology? In: Staab, S., Studer, R. (eds.) Handbook on Ontologies. IHIS, pp. 1–17. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-540-92673-3_0

    Chapter  Google Scholar 

  26. 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 (2021)

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  30. Yahya, M., et al.: Towards generalized welding ontology in line with ISO and knowledge graph construction. In: ESWC (Posters & Demos) (2022)

    Google Scholar 

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Acknowledgements

The work was partially supported by 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.

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Authors and Affiliations

  1. Bosch Center for Artificial Intelligence, Renningen, Germany

    Dongzhuoran Zhou, Zhuoxun Zheng & Evgeny Kharlamov

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

    Dongzhuoran Zhou, Baifan Zhou, Egor V. Kostylev, Ernesto Jiménez-Ruiz & Evgeny Kharlamov

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

    Zhuoxun Zheng & Ahmet Soylu

  4. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China

    Gong Cheng

  5. City, University of London, London, UK

    Ernesto Jiménez-Ruiz

Authors
  1. Dongzhuoran Zhou
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  2. Baifan Zhou
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  3. Zhuoxun Zheng
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  4. Egor V. Kostylev
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  5. Gong Cheng
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  6. Ernesto Jiménez-Ruiz
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  7. Ahmet Soylu
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  8. Evgeny Kharlamov
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Correspondence to Dongzhuoran Zhou .

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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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Zhou, D. et al. (2022). Enhancing Knowledge Graph Generation with Ontology Reshaping – Bosch Case. 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_45

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  • DOI: https://doi.org/10.1007/978-3-031-11609-4_45

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