Knowledge Graphs for Semantically Integrating Cyber-Physical Systems

  • Irlán Grangel-GonzálezEmail author
  • Lavdim Halilaj
  • Maria-Esther Vidal
  • Omar Rana
  • Steffen Lohmann
  • Sören Auer
  • Andreas W. Müller
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11029)


Cyber-Physical Systems (CPSs) are engineered systems that result from the integration of both physical and computational components designed from different engineering perspectives (e.g., mechanical, electrical, and software). Standards related to Smart Manufacturing (e.g., AutomationML) are used to describe CPS components, as well as to facilitate their integration. Albeit expressive, smart manufacturing standards allow for the representation of the same features in various ways, thus hampering a fully integrated description of a CPS component. We tackle this integration problem of CPS components and propose an approach that captures the knowledge encoded in smart manufacturing standards to effectively describe CPSs. We devise SemCPS, a framework able to combine Probabilistic Soft Logic and Knowledge Graphs to semantically describe both a CPS and its components. We have empirically evaluated SemCPS on a benchmark of AutomationML documents describing CPS components from various perspectives. Results suggest that SemCPS enables not only the semantic integration of the descriptions of CPS components, but also allows for preserving the individual characterization of these components.



This work has partly been supported by the German Federal Ministry of Education, Research (BMBF) in the context of the project Industrial Data Space Plus (grant no. 01IS17031), and EU H2020 Programme for the project BOOST 4.0 (grant no. 780732).


  1. 1.
    Bach, S.H., Broecheler, M., Huang, B., Getoor, L.: Hinge-loss markov random fields and probabilistic soft logic. J. Mach. Learn. Res. (JMLR) 18, 1–67 (2017)MathSciNetzbMATHGoogle Scholar
  2. 2.
    Bauernhansl, T., ten Hompel, M., Vogel-Heuser, B.: Industrie 4.0 in Produktion, Automatisierung und Logistik: Anwendung, Technologien, Migration. Springer, Wiesbaden (2014). Scholar
  3. 3.
    Bi, L., Jiao, Z.: An information integration framework based on XML to support mechatronics multi-disciplinary design. In: IEEE Conference on Robotics, Automation and Mechatronics, RAM, China, pp. 175–179 (2008)Google Scholar
  4. 4.
    Biffl, S., Kovalenko, O., Lüder, A., Schmidt, N., Rosendahl, R.: Semantic mapping support in AutomationML. In: ETFA, pp. 1–4. IEEE (2014)Google Scholar
  5. 5.
    Bröcheler, M., Mihalkova, L., Getoor, L.: Probabilistic similarity logic. In: Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence, UAI 2010, Catalina Island, CA, USA, pp. 73–82 (2010)Google Scholar
  6. 6.
    Chekol, M.W., Pirrò, G., Schoenfisch, J., Stuckenschmidt, H.: Marrying uncertainty and time in knowledge graphs. In: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, California, USA, pp. 88–94 (2017)Google Scholar
  7. 7.
    Chen, K., Bankston, J., Panchal, J.H., Schaefer, D.: A framework for integrated design of mechatronic systems. In: Wang, L., Nee, A. (eds.) Collaborative Design and Planning for Digital Manufacturing, pp. 37–70. Springer, London (2009). Scholar
  8. 8.
    Drath, R.: Datenaustausch in der Anlagenplanung mit AutomationML: Integration von CAEX, PLCopen XML und COLLADA. Springer, Heidelberg (2009)Google Scholar
  9. 9.
    Estévez-Estévez, E., Marcos, M., Lüder, A., Hundt, L.: PLCopen for achieving interoperability between development phases. In: Proceedings of 15th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA, Spain, pp. 1–8 (2010)Google Scholar
  10. 10.
    OPC Foundation. OPC Unified Architecture Specification. Part 1: Overview and Concepts (2015)Google Scholar
  11. 11.
    Grangel-González, I., et al.: Alligator: a deductive approach for the integration of industry 4.0 standards. In: Blomqvist, E., Ciancarini, P., Poggi, F., Vitali, F. (eds.) EKAW 2016. LNCS (LNAI), vol. 10024, pp. 272–287. Springer, Cham (2016). Scholar
  12. 12.
    Gutierrez, C., Hurtado, C.A., Mendelzon, A.O., Pérez, J.: Foundations of semantic web databases. J. Comput. Syst. Sci. 77(3), 520–541 (2011)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Huber, J., Niepert, M., Noessner, J., Schoenfisch, J., Meilicke, C., Stuckenschmidt, H.: An infrastructure for probabilistic reasoning with web ontologies. Semant. Web 8(2), 255–269 (2017)CrossRefGoogle Scholar
  14. 14.
    Jacoby, M., Antonić, A., Kreiner, K., Łapacz, R., Pielorz, J.: Semantic interoperability as key to IoT platform federation. In: Podnar Žarko, I., Broering, A., Soursos, S., Serrano, M. (eds.) InterOSS-IoT 2016. LNCS, vol. 10218, pp. 3–19. Springer, Cham (2017). Scholar
  15. 15.
    Jirkovský, V., Obitko, M., Marík, V.: Understanding data heterogeneity in the context of cyber-physical systems integration. IEEE Trans. Ind. Inform. 13(2), 660–667 (2017)CrossRefGoogle Scholar
  16. 16.
    Kimmig, A., Bach, S., Broecheler, M., Huang, B., Getoor, L.: A short introduction to Probabilistic Soft Logic. In: Proceedings of the NIPS Workshop on Probabilistic Programming: Foundations and Applications, pp. 1–4 (2012)Google Scholar
  17. 17.
    Kovalenko, O., Euzenat, J.: Semantic matching of engineering data structures. In: Biffl, S., Sabou, M. (eds.) Semantic Web Technologies for Intelligent Engineering Applications, pp. 137–157. Springer, Cham (2016). Scholar
  18. 18.
    Lange, C.: Krextor - an extensible XML\(\rightarrow \)RDF extraction framework. In: Scripting and Development for the Semantic Web (SFSW). CEUR Workshop Proceedings, vol. 449, Aachen, May 2009Google Scholar
  19. 19.
    Li, Q., Jiang, H., Tang, Q., Chen, Y., Li, J., Zhou, J.: Smart manufacturing standardization: reference model and standards framework. In: Ciuciu, I., et al. (eds.) OTM 2016. LNCS, vol. 10034, pp. 16–25. Springer, Cham (2017). Scholar
  20. 20.
    Lüder, A., Schmidt, N., Rosendahl, R., John, M.: Integrating different information types within AutomationML. In: Proceedings of the IEEE Emerging Technology and Factory Automation, ETFA, Spain, pp. 1–5 (2014)Google Scholar
  21. 21.
    Sabou, M., Ekaputra, F.J., Biffl, S.: Semantic web technologies for data integration in multi-disciplinary engineering. In: Biffl, S., Lüder, A., Gerhard, D. (eds.) Multi-Disciplinary Engineering for Cyber-Physical Production Systems, pp. 301–329. Springer, Cham (2017). Scholar
  22. 22.
    Mordinyi, R., Winkler, D., Ekaputra, F.J., Wimmer, M., Biffl, S.: Investigating model slicing capabilities on integrated plant models with AutomationML. In: Proceedings of 21st IEEE International Conference on Emerging Technologies and Factory Automation, ETFA, Germany, pp. 1–8 (2016)Google Scholar
  23. 23.
    Moser, T., Mordinyi, R., Winkler, D.: Extending mechatronic objects for automation systems engineering in heterogeneous engineering environments. In: Proceedings of IEEE 17th International Conference on Emerging Technologies & Factory Automation, ETFA, Poland, pp. 1–8 (2012)Google Scholar
  24. 24.
    Prinz, J.: Consistent merging of AutomationML documents in multiple sources scenarios. In: 4th AutomationML User Conference, Germany (2016)Google Scholar
  25. 25.
    Prösser, M., Moore, P.R., Chen, X., Wong, C., Schmidt, U.: A new approach towards systems integration within the mechatronic engineering design process of manufacturing systems. Int. J. Comput. Integr. Manuf. 26(8), 806–815 (2013)CrossRefGoogle Scholar
  26. 26.
    Pujara, J., Getoor, L.: Generic statistical relational entity resolution in knowledge graphs. CoRR, abs/1607.00992 (2016)Google Scholar
  27. 27.
    Ridgway, K., Clegg, C., Williams, D.: The Factory of the Future, Future Manufacturing Project: Evidence Paper 29. Foresight, Government Office for Science, London (2013)Google Scholar
  28. 28.
    Sabou, M., Ekaputra, F., Kovalenko, O., Biffl, S.: Supporting the engineering of cyber-physical production systems with the AutomationML analyzer. In: 1st International Workshop on Cyber-Physical Production Systems (CPPS), pp. 1–8. IEEE (2016)Google Scholar
  29. 29.
    Scharffe, F., Zimmermann, A.: D2. 2.10: Expressive alignment language and implementation. Deliverable D2, 2 (2007)Google Scholar
  30. 30.
    Schleipen, M., Gutting, D., Sauerwein, F.: Domain dependant matching of MES knowledge and domain independent mapping of AutomationML models. In: Proceedings of IEEE 17th International Conference on Emerging Technologies & Factory Automation, ETFA, Poland, pp. 1–7 (2012)Google Scholar
  31. 31.
    Schmidt, N., Lüder, A., Rosendahl, R., Ryashentseva, D., Foehr, M., Vollmar, J.: Surveying integration approaches for relevance in cyber physical production systems. In: 20th IEEE Conference on Emerging Technologies & Factory Automation, ETFA, Luxembourg, pp. 1–8 (2015)Google Scholar
  32. 32.
    Volz, J., Bizer, C., Gaedke, M., Kobilarov, G.: Silk - a link discovery framework for the web of data. In: Proceedings of the WWW 2009 Workshop on Linked Data on the Web, LDOW, Madrid, Spain, 20 April 2009Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Irlán Grangel-González
    • 1
    • 2
    Email author
  • Lavdim Halilaj
    • 1
    • 2
  • Maria-Esther Vidal
    • 3
    • 4
  • Omar Rana
    • 1
  • Steffen Lohmann
    • 2
  • Sören Auer
    • 3
    • 4
  • Andreas W. Müller
    • 5
  1. 1.Enterprise Information Systems (EIS)University of BonnBonnGermany
  2. 2.Fraunhofer Institute for Intelligent Analysis and Information Systems (IAIS)Sankt AugustinGermany
  3. 3.L3S Research CenterHanoverGermany
  4. 4.TIB Leibniz Information Center for Science and TechnologyHanoverGermany
  5. 5.Schaeffler TechnologiesHerzogenaurachGermany

Personalised recommendations