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Literal-Aware Knowledge Graph Embedding for Welding Quality Monitoring: A Bosch Case

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The Semantic Web – ISWC 2023 (ISWC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14266))

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Abstract

Recently there has been a series of studies in knowledge graph embedding (KGE), which attempts to learn the embeddings of the entities and relations as numerical vectors and mathematical mappings via machine learning (ML). However, there has been limited research that applies KGE for industrial problems in manufacturing. This paper investigates whether and to what extent KGE can be used for an important problem: quality monitoring for welding in manufacturing industry, which is an impactful process accounting for production of millions of cars annually. The work is in line with Bosch research of data-driven solutions that intends to replace the traditional way of destroying cars, which is extremely costly and produces waste. The paper tackles two very challenging questions simultaneously: how large the welding spot diameter is; and to which car body the welded spot belongs to. The problem setting is difficult for traditional ML because there exist a high number of car bodies that should be assigned as class labels. We formulate the problem as link prediction, and experimented popular KGE methods on real industry data, with consideration of literals. Our results reveal both limitations and promising aspects of adapted KGE methods.

Code and data are available under https://github.com/boschresearch/KGE-Welding.

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Acknowledgements

The work was partially supported by EU projects Dome 4.0 (953163), OntoCommons (958371), DataCloud (101016835), Graph Massivizer (101093202) and enRichMyData (101093202) and the SIRIUS Centre (237898) funded by Norwegian Research Council.

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Authors and Affiliations

  1. Bosch Center for AI, Renningen, Germany

    Zhipeng Tan, Zhuoxun Zheng, Irlan-Grangel Gonzalez & Evgeny Kharlamov

  2. RWTH Aachen University, Aachen, Germany

    Zhipeng Tan & Ziqi Huang

  3. Department of Informatics, University of Oslo, Oslo, Norway

    Baifan Zhou, Zhuoxun Zheng & Evgeny Kharlamov

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

    Baifan Zhou & Ahmet Soylu

  5. Department of Computer Science, Free University of Bozen-Bolzano, Bolzano, Italy

    Ognjen Savkovic

Authors
  1. Zhipeng Tan
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  2. Baifan Zhou
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  3. Zhuoxun Zheng
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  4. Ognjen Savkovic
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  7. Ahmet Soylu
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  8. Evgeny Kharlamov
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Corresponding authors

Correspondence to Zhipeng Tan , Baifan Zhou or Evgeny Kharlamov .

Editor information

Editors and Affiliations

  1. University of Liverpool, Liverpool, UK

    Terry R. Payne

  2. University of Bologna, Bologna, Italy

    Valentina Presutti

  3. Southeast University, Nanjing, China

    Guilin Qi

  4. Universidad Politécnica de Madrid, Madrid, Spain

    María Poveda-Villalón

  5. Huawei Technologies R&D UK, Edinburgh, UK

    Giorgos Stoilos

  6. Centrum Wiskunde and Informatica, Amsterdam, The Netherlands

    Laura Hollink

  7. IT University of Copenhagen, Copenhagen, Denmark

    Zoi Kaoudi

  8. Nanjing University, Nanjing, China

    Gong Cheng

  9. Tsinghua University, Beijing, China

    Juanzi Li

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Tan, Z. et al. (2023). Literal-Aware Knowledge Graph Embedding for Welding Quality Monitoring: A Bosch Case. In: Payne, T.R., et al. The Semantic Web – ISWC 2023. ISWC 2023. Lecture Notes in Computer Science, vol 14266. Springer, Cham. https://doi.org/10.1007/978-3-031-47243-5_25

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  • DOI: https://doi.org/10.1007/978-3-031-47243-5_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-47242-8

  • Online ISBN: 978-3-031-47243-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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