Skip to main content

A Knowledge Management Approach Supporting Model-Based Systems Engineering

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 1366)


Model-based Systems Engineering (MBSE) is a noval approach to support complex system development by formalizing system artifacts and development using models. Though MBSE models provide a completely structural formalisms about system development for system developers, such large of domain specific knowledge represented by models cannot be captured as what the developers expect. This leads to a big challenge when MBSE can be widely used for complex system development. In this paper, a knowledge management approach is proposed to support an intelligent question answering scenario when implementing MBSE in system lifecycle. We make use of the GOPPRR approach to support MBSE formalisms which are transformed to knowledge graph models. Then such models provide cues for intelligent question answers through reasoning. In the case study, we make use of an auto-braking system scenario to develop MBSE models and to implement the intelligent question answering. Finally, we find the availability of our approach is evaluated which the domain engineers enable to capture their domain knowledge more efficiently.


  • Knowledge management
  • Model-based system engineering
  • Knowledge graph modeling
  • Knowledge reasoning

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-72651-5_55
  • Chapter length: 10 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
USD   219.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-72651-5
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   279.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.


  1. 1.

    The details are proposed in [18].


  1. Baclawski, K., Kokar, M.K., Kogut, P.A., Hart, L., Smith, J., Holmes, W.S., Letkowski, J., Aronson, M.L.: Extending UML to support ontology engineering for the semantic web. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2001)

    Google Scholar 

  2. Chinosi, M., Trombetta, A.: BPMN: an introduction to the standard. Comput. Stan. Interfaces 34(1), 124–134 (2012)

    CrossRef  Google Scholar 

  3. Cuenot, P., Chen, D., Gerard, S., Lonn, H., Reiser, M.O., Servat, D., Sjostedt, C.J., Tavakoli Kolagari, R., Torngren, M., Weber, M.: Managing complexity of automotive electronics using the EAST-ADL. In: 12th IEEE International Conference on Engineering Complex Computer Systems (ICECCS 2007), pp. 353–358, no. Iceccs. IEEE, July 2007.

  4. Estefan, J.: MBSE methodology survey. Insight 12, 16–18 (2009)

    Google Scholar 

  5. Gruber, T.R.: A translation approach to portable ontology specifications. Knowl. Acquisition 5(2), 199–220 (1993)

    CrossRef  Google Scholar 

  6. Holt, J., Perry, S.: Sysml for Systems Engineering. Bibliovault OAI Repository, The University of Chicago Press, Chicago (2008)

    Google Scholar 

  7. Hu, Z., Lu, J., Chen, J., Zheng, X., Kyritsis, D., Zhang, H.: A complexity analysis approach for model-based system engineering. In: 2020 IEEE 15th International Conference of System of Systems Engineering (SoSE), pp. 000501–000506. IEEE, June 2020.

  8. International Council on Systems Engineering (INCOSE): Systems Engineering Vision 2020. Systems Engineering Vision 2020 (September), vol. 32 (2007).

  9. Kern, H., Hummel, A., Kühne, S.: Towards a comparative analysis of meta-metamodels. In: Proceedings of the Compilation of the Co-located Workshops on DSM’11, TMC’11, AGERE!’11, AOOPES’11, NEAT’11, & VMIL’11 - SPLASH ’11 Workshops, vol. 1, p. 7. ACM Press, New York, USA (2011)

    Google Scholar 

  10. Lu, J., Wang, G., Ma, J., Kiritsis, D., Zhang, H., Törngren, M.: General modeling language to support model–based systems engineering formalisms (Part 1). In: INCOSE International Symposium (2020)

    Google Scholar 

  11. Mann, C.: A practical guide to SysML: the systems modeling language. Kybernetes, vol. 38, no. (1/2) (2009).

  12. McDermott, T., DeLaurentis, D., Beling, P., Blackburn, M., Bone, M.: AI4SE and SE4AI: a research roadmap. Insight 23(1), 8–14 (2020).

  13. Nickel, M., Murphy, K., Tresp, V., Gabrilovich, E.: A review of relational machine learning for knowledge graphs. Proc. IEEE 104(1), 11–33 (2016).,,

  14. O’Connor, M., Das, A.: SQWRL: a query language for OWL. In: CEUR Workshop Proceedings (2009)

    Google Scholar 

  15. O’Connor, M.J., Das, A.K.: A method for representing and querying temporal information in OWL. In: Communications in Computer and Information Science (2011)

    Google Scholar 

  16. Schmidt, J., Rudolph, S.: Gaining system design knowledge by systematic design space exploration with graph based design languages. In: AIP Conference Proceedings, pp. 390–393 (2014).

  17. Spangelo, S.C., Kaslow, D., Delp, C., Cole, B., Anderson, L., Fosse, E., Gilbert, B.S., Hartman, L., Kahn, T., Cutler, J.: Applying model based systems engineering (MBSE) to a standard CubeSat. In: 2012 IEEE Aerospace Conference, pp. 1–20. IEEE (2012).

  18. Wang, H., Wang, G., Lu, J., Ma, C.: Ontology supporting model-based systems engineering based on a GOPPRR approach. In: Advances in Intelligent Systems and Computing, vol. 930, pp. 426–436 (2019).,

  19. Wang, H., Wang, G., Lu, J., Ma, C.: Ontology supporting model-based systems engineering based on a gopprr approach. In: Rocha, Á., Adeli, H., Reis, L.P., Costanzo, S. (eds.) New Knowledge in Information Systems and Technologies, pp. 426–436. Springer International Publishing, Cham (2019)

    CrossRef  Google Scholar 

  20. Wang, X.H., Zhang, D.Q., Gu, T., Pung, H.J.: Ontology based context modeling and reasoning using OWL. In: IEEE Annual Conference on Pervasive Computing and Communications Workshops, 2004. Proceedings of the Second, pp. 18–22. IEEE (2004).

Download references


The work presented in this paper is supported by the EU H2020 project (869951) FACTLOG-Energy-aware Factory Analytics for Process Industries, EU H2020 project (825030) QU4LITY Digital Reality in Zero Defect Manufacturing and the InnoSwiss IMPULSE project on Digital Twins.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Jinzhi Lu .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Yang, P., Lu, J., Feng, L., Wu, S., Wang, G., Kiritsis, D. (2021). A Knowledge Management Approach Supporting Model-Based Systems Engineering. In: Rocha, Á., Adeli, H., Dzemyda, G., Moreira, F., Ramalho Correia, A.M. (eds) Trends and Applications in Information Systems and Technologies . WorldCIST 2021. Advances in Intelligent Systems and Computing, vol 1366. Springer, Cham.

Download citation