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Digital Twin Data Broker with Assisted Mapping into a Knowledge Base

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Innovative Intelligent Industrial Production and Logistics (IN4PL 2024)

Abstract

The frequent usage of digital twins to communicate between physical objects is resulting in more complex cyber-physical systems. To simplify the individual components’ integration and to optimize their usage, a data broker is being developed. Therefore, digital twins need to be semantically organized in an ontology that provides the advantage of reasoning methods. An assisted workflow is being developed to automatically enter subgraphs into an ontology. As a digital twin representation, the Asset Administration Shell format is used to have an international standard technology. Based on this, a new domain-specific language is developed, allowing experts to configure the generation process. This process maps the digital twin’s information into a graph representation of the ontology. The preconfigured generation process enables the user to efficiently register new digital twins without having expert knowledge of the underlying ontology. Additionally, a Large Language Model vector embedding and text reasoning support is implemented analysing the digital twin to create entity suggestions. The presented data broker is an automation tool for bridging the gap between semantic descriptions and digital twin formats in order to unite the advantages of both representations.

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References

  1. Grüner S., Neidig J., Orzelski A., Pollmeier S.: Asset administration shell reading guide, March 2023. https://industrialdigitaltwin.org/wp-content/uploads/2022/11/2022-11-03_IDTA_AAS-Reading-Guide.pdf. Accessed 12 Apr 2024

  2. Booshehri M., Emele L., et al.: Introducing the open energy ontology: enhancing data interpretation and interfacing in energy systems analysis. Energy AI 5, 100074 (2021). ISSN 2666-5468. https://doi.org/10.1016/j.egyai.2021.100074

  3. Meiser M., Duppe B., Zinnikus I.: Generation of meaningful synthetic sensor data - evaluated with a reliable transferability methodology. Energy AI 15 (2023). https://doi.org/10.1016/j.egyai.2023.100308

  4. Brandherm, B., Deru, M., Ndiaye, A, Kiefer, G., Baus, J., Gampfer, R.: Integration of renewable energies—AI-based prediction methods for electricity generation from photovoltaic systems. In: Barton, T., Müller, C. (eds.) Apply Data Science: Introduction, Applications and Projects, GER 2023, pp. 137–158. Springer Fachmedien, Wiesbaden, Germany (2023). ISBN 978-3-658-38798-3. https://doi.org/10.1007/978-3-658-38798-3_9

  5. Schmeyer, T.A., et al.: Assistance system for ai-based monitoring and prediction in smart grids. In: 2023 Human Computer Interaction International Conferences (HCII-2023). Springer, Cham (2023). https://doi.org/10.1007/978-3-031-36001-5_65

  6. European Commission: Accelerating the digital transformation of the European energy system. https://digital-strategy.ec.europa.eu/en/policies/digitalisation-energy. Accessed 12 Apr 2024

  7. Ismail, F.B., Al-Faiz, H., Hasini, H., Al-Bazi, A., Kazem, H.A.: A comprehensive review of the dynamic applications of the digital twin technology across diverse energy sectors. Energy Strategy Rev. 52, 101334 (2024). ISSN 2211-467X. https://doi.org/10.1016/j.esr.2024.101334

  8. Bader, S., Maleshkova, M.: The semantic asset administration shell (2019). https://doi.org/10.1007/978-3-030-33220-4_12

  9. Huang Y., Dhouib S., Medinacelli L. P., Malenfant J.: Enabling semantic interoperability of asset administration shells through an ontology-based modeling method. In: Proceedings of the 25th International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings, MODELS 2022, pp. 497–502. Association for Computing Machinery, New York, NY, USA (2022). https://doi.org/10.1145/3550356.3561606

  10. Rongen, S., Nikolova, N., van der Pas, M.: Modelling with AAS and RDF in Industry 4.0. Comput. Ind. 148, 103910 (2023). ISSN 0166-3615. https://doi.org/10.1016/j.compind.2023.103910

  11. Lehnert, C., Engel, G., Steininger, H., Drath, R., Greiner, T.: A hierarchical domain-specific language for cyber-physical production systems integrating asset administration shells. In: IEEE 26th International Conference on Emerging Technologies and Factory Automation (ETFA), Vasteras, Sweden, SWE, pp. 01–04 (2021)

    Google Scholar 

  12. Xia, Y., Xiao, Z., Jazdi, N., Weyrich, M.: Generation of asset administration shell with large language model agents: towards semantic interoperability in digital twins in the context of Industry 4.0, March 2024. https://arxiv.org/abs/2403.17209

  13. Luxenburger, A., Porta, D., Knoch, S., Mohr, J., Schwartz, T.: A service infrastructure for Industrie 4.0 testbeds based on asset administration shells. In: IEEE 28th International Conference on Emerging Technologies and Factory Automation (ETFA), Sinaia, Romania, ROU, pp. 1–8 (2023). https://doi.org/10.1109/ETFA54631.2023.10275335

  14. Industrial Digital Twin Association e.V., AAS-Specs: Resource Description Framework. https://github.com/admin-shell-io/aas-specs/tree/master/schemas/rdf. Accessed 29 May 2024

  15. Spear, A., Ceusters, W., Smith, B.: Functions in basic formal ontology. Appl. Ontol., 103–128 (2016)

    Google Scholar 

  16. Industrial Digital Twin Association: IDTA Submodel Templates published. https://industrialdigitaltwin.org/en/news-dates/publication-of-idta-submodel-templates-4071. Accessed 03 Jun 2024

  17. Neo4j: neosemantics (n10s): Neo4j RDF & Semantics toolkit. https://neo4j.com/labs/neosemantics/. Accessed 16 May 2024

  18. Singhal, A., Google, Inc.: Modern information retrieval: a brief overview. IEEE Data Eng. Bull. 24, 35–44 (2021)

    Google Scholar 

  19. Hugging Face: BERT-base-uncased. https://huggingface.co/google-bert/bert-base-uncased. Accessed 17 May 2024

  20. Bechhofer, S., et al.: W3C-OWL web ontology language reference. https://www.w3.org/TR/owl-ref/. Accessed 17 May 2024

  21. Saryerwinnie, J.: JMESPath: JMESPath Libraries. https://jmespath.org/libraries.html. Accessed 23 May 2024

  22. National Academy of Science and Engineering: Gaia-X Hub Germany. https://gaia-x-hub.de/en/. Accessed 23 May 2024

  23. Eclipse: GitHub: AASX Package Explorer. https://github.com/eclipse-aaspe/package-explorer. Accessed 24 May 2024

  24. Eclipse BaSyx\(^{TM}\): GitHub: eclipse-basyx. https://github.com/eclipse-basyx. Accessed 24 May 2024

  25. Chase, A., et al.: Multimodales Fenster in die Vergangenheit der ehemaligen Vau-ban-Festung Saarlouis mittels ChatGPT. In: Barton T., Müller C. (eds.) Angewandte Wirtschaftsinformatik - Generative KI im Kontext der Wirtschaftsinformatik, December 2024, pp. 1–19, Springer Vieweg, Wiesbaden, Germany, GER (2024, to appear)

    Google Scholar 

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Acknowledgments

This work has been founded by the Federal Ministry for Economic Affairs and Climate Action in the context of the Project IdFlexNetz (FKZ 03EI6067A-E). Special thanks to Julia Bayer and Daniel Rohrbach for corrections and to our project partners VSE AG, Schneider Electric and Spherity.

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Correspondence to Thomas Schmeyer .

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Schmeyer, T., Krämer, K., Peh, AL., Brandherm, B., Chikobava, M., Kiefer, GL. (2025). Digital Twin Data Broker with Assisted Mapping into a Knowledge Base. In: Dassisti, M., Madani, K., Panetto, H. (eds) Innovative Intelligent Industrial Production and Logistics. IN4PL 2024. Communications in Computer and Information Science, vol 2372. Springer, Cham. https://doi.org/10.1007/978-3-031-80760-2_2

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  • DOI: https://doi.org/10.1007/978-3-031-80760-2_2

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