Hostname: page-component-848d4c4894-8bljj Total loading time: 0 Render date: 2024-07-01T12:09:31.082Z Has data issue: false hasContentIssue false
  • English
  • Français

Automatic knowledge graph creation from engineering standards using the example of formulas

Published online by Cambridge University Press:  16 May 2024

Janosch Luttmer ,
Mostafa Kandel ,
Dominik Ehring  and
Arun Nagarajah
Janosch Luttmer*
Affiliation:
University of Duisburg-Essen, Germany
Mostafa Kandel
Affiliation:
University of Duisburg-Essen, Germany
Dominik Ehring
Affiliation:
University of Duisburg-Essen, Germany
Arun Nagarajah
Affiliation:
University of Duisburg-Essen, Germany
*
janosch.luttmer@uni-due.de

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Engineering standards are an important source of knowledge in product development. Despite the increasing digitalisation, the provision and usage of standards is characterised by lots of manual steps. This research paper aims at applying automatic knowledge graph creation in the domain of engineering standards to enable machine-actionable standards. For this, a formula knowledge graph ontology as well as suitable information extraction techniques are developed. The concept is validated using the example of DIN ISO 281, showing the overall capability of automatic knowledge graph creation.

Keywords

engineering standardsinformation extractionknowledge representationsontologyartificial intelligence (AI)
Type
Design Information and Knowledge
Information
Proceedings of the Design Society , Volume 4: DESIGN 2024 , May 2024 , pp. 423 - 432
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2024.

References

Adnan, K. and Akbar, R. (2019), “An analytical study of information extraction from unstructured and multidimensional big data”, Journal of Big Data, Vol. 6 No. 1. https://doi.org/10.1186/s40537-019-0254-8CrossRefGoogle Scholar
Bender, B. and Gericke, K. (Eds.) (2021), Pahl/Beitz Konstruktionslehre: Methoden und Anwendung erfolgreicher Produktentwicklung, 9th ed., Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-57303-7Google Scholar
Czarny, D.A., Kriegsmann, S., Nagarajah, A., Schacht, M. and Sporer, R. (2022), Use Cases for SMART Standards, available at: https://www.din.de/resource/blob/868896/33ab0714368ab3cbb4ebe8614f2b065a/whitepaper-idis-2-en-data.pdf (accessed 15 November 2023).Google Scholar
Dumitrescu, R., Albers, A., Riedel, O., Stark, R. and Gausemeier, J. (2021), Engineering in Deutschland - Status quo in Wirtschaft und Wissenschaft: Ein Beitrag zum Advanced Systems Engineering, Paderborn.Google Scholar
Ehring, D., Ferraz-Doughty, P., Luttmer, J. and Nagarajah, A. (2023), “A first step towards automatic identification and provision of user-specific knowledge: A verification of the feasibility of automatic text classification using the example of standards”, Procedia CIRP, Vol. 119. https://doi.org/10.1016/j.procir.2023.02.183CrossRefGoogle Scholar
Ehring, D., Luttmer, J., Pluhnau, R. and Nagarajah, A. (2021), “SMART standards - concept for the automated transfer of standard contents into a machine-actionable form”, Procedia CIRP, Vol. 100, pp. 163–168. https://doi.org/10.1016/j.procir.2021.05.025CrossRefGoogle Scholar
Gräßler, I. and Hesse, P. (2023), “Considering Engineering Activities and Product Characteristics to achieve Material Circularity by Design”, Proceedings of the Design Society, Vol. 3. https://doi.org/10.1017/pds.2023.108CrossRefGoogle Scholar
Hicks, B.J., Culley, S.J., Allen, R.D. and Mullineux, G. (2002), “A framework for the requirements of capturing, storing and reusing information and knowledge in engineering design”, IJIM, Vol. 22 No. 4, pp. 263–280. https://doi.org/10.1016/S0268-4012(02)00012-9Google Scholar
Huang, Y., Yu, S., Chu, J., Su, Z., Cong, Y., Wang, H. and Fan, H. (2023), “Combining Deep Learning with Knowledge Graph for Design Knowledge Acquisition in Conceptual Product Design”, Computer Modeling in Engineering & Sciences, Vol. 138 No. 1, pp. 167–200. https://doi.org/10.32604/cmes.2023.028268Google Scholar
Huet, A., Pinquié, R., Segonds, F. and Véron, P. (2023), “A cognitive design assistant for context-aware computer-aided design”, Procedia CIRP, Vol. 119, pp. 1029–1034. https://doi.org/10.1016/j.procir.2023.03.146CrossRefGoogle Scholar
Isaksson, O. and Eckert, C. (2020), Product Development 2040: Technologies are just as good as the Designer´s ability to integrate them. https://doi.org/10.35199/report.pd2040CrossRefGoogle Scholar
Layer, M., Neubert, S., Tiemann, L. and Stelzer, R. (2023), “Identification and Retrieval of relevant Information for instantiating Digital Twins during the Construction of Process Plants”, Proceedings of the Design Society, Vol. 3, pp. 2175–2184. https://doi.org/10.1017/pds.2023.218Google Scholar
Loibl, A., Manoharan, T. and Nagarajah, A. (2020), “Procedure for the transfer of standards into machine-actionability”, JAMDSM, Vol. 14 No. 2. https://doi.org/10.1299/jamdsm.2020jamdsm0022CrossRefGoogle Scholar
Luttmer, J., Ehring, D., Pluhnau, R., Kocks, C. and Nagarajah, A. (2022), “SMART Standards: Modularization Approach for Engineering Standards”, in Proceedings of ASME 2022 IDETC-CIE 2022, The American Society of Mechanical Engineers, New York, N.Y. https://doi.org/10.1115/DETC2022-88206CrossRefGoogle Scholar
Luttmer, J., Ehring, D., Pluhnau, R. and Nagarajah, A. (2021), “Representation and Application of Digital Standards using Knowledge Graphs”, Proceedings of the Design Society: ICED21, Vol. 1, pp. 2551–2560. https://doi.org/10.1017/pds.2021.516CrossRefGoogle Scholar
Luttmer, J., Prihodko, V., Ehring, D. and Nagarajah, A. (2023), “Requirements extraction from engineering standards – systematic evaluation of extraction techniques”, Procedia CIRP, Vol. 119, pp. 794–799. https://doi.org/10.1016/j.procir.2023.03.125CrossRefGoogle Scholar
Manoharan, T., Loibl, A., Nagarajah, A. and Köhler, P. (2019), “Approach for a Machine-Interpretable Provision of Standard Contents Using Welded Constructions as an Example”, Proceedings of the Design Society: ICED 19, Vol. 1 No. 1, pp. 2477–2486. https://doi.org/10.1017/dsi.2019.254Google Scholar
Noy, N.F. and McGuiness, D.L. (2001), Ontology Development 101: A Guide to Creating Your First Ontology.Google Scholar
Schubmehl, D. and Vesset, D. (2014), The Knowledge Quotient: Unlocking the Hidden Value of Information Using Search and Content Analytics.Google Scholar
Schuch, W. and Wischhoefer, C. (2018), “Abschluss des Projektes XML100”, DIN Mitteilungen, 2018.Google Scholar
Siddharth, L., Blessing, L.T.M., Wood, K.L. and Luo, J. (2022), “Engineering Knowledge Graph From Patent Database”, J. Comp. Inf. Sci. Eng., Vol. 22 No. 2. https://doi.org/10.1115/1.4052293CrossRefGoogle Scholar
Tamašauskaitė, G. and Groth, P. (2023), “Defining a Knowledge Graph Development Process Through a Systematic Review”, ACM Trans. Softw. Eng. Methodol., Vol. 32 No. 1, pp. 1–40. https://doi.org/10.1145/3522586CrossRefGoogle Scholar
Zhao, H., Pan, Y. and Yang, F. (2020), “Research on Information Extraction of Technical Documents and Construction of Domain Knowledge Graph”, IEEE Access. https://doi.org/10.1109/ACCESS.2020.3024070CrossRefGoogle Scholar
Zhenyong, W., Xinguo, M., Lina, H. and Goh, M. (2020), “Product Development-Oriented Knowledge Service: Status Review, Framework, and Solutions”, IEEE Access, Vol. 8, pp. 64442–64460. https://doi.org/10.1109/ACCESS.2020.2984631CrossRefGoogle Scholar