Semantic Web Framework for Rule-Based Generation of Knowledge and Simulation of Manufacturing Systems

  • Markus Rabe
  • Pavel Gocev


The development of new products and manufacturing systems is usually performed in the form of projects. Frequently, the conduction of the project takes more time than planned due to inconsistency, incompleteness, and redundancy of data, which delays other project activities influencing the start of production (SOP). This paper proposes a semantic Web framework for cooperation and interoperability within product design and manufacturing engineering projects. Data and knowledge within the manufacturing domain are modelled within ontologies applying rule-based mapping. The framework facilitates the generation of new knowledge through rule based inference that enriches the ontology. This enables a high-level model completeness in the early phase of product design and manufacturing system development, which is a basic prerequisite for the realisation of a proper simulation study and analysis. The simulation results can be integrated into the ontologies as a knowledge that additionally extends the ontology.


Semantic Web Product Design Manufacturing Ontology Knowledge Base Rules Inference Modelling and Simulation 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    Gocev, P., Rabe, M.: Simulation Models for Factory Planning through Connection of ERP and MES Systems. Tagungsband 12. ASIM-Fachtagung Simulation in Produktion und Logistik, pp. 223–232. Kassel (2006)Google Scholar
  2. [2]
    Gocev, P.: Semantic Web Technologies for Simulation in Production and Logistic-a Survey. Simulation und Visualisierung 2007 — Doktorandenforum Diskrete Simulation, pp. 1–10. Magdeburg (2007)Google Scholar
  3. [3]
    Silver, G., Hassan, O., Miller, J.: From Domain Ontologies to Modeling Ontologies to Executable Simulation Models. Proceedings of the 2007 Winter Simulation Conference, pp 1108–1117. (2007)Google Scholar
  4. [4]
    Miller, J., Fischwick, P.: Investigating Ontologies for Simulation Modelling. Proceedings of the 37th Annual Simulation Symposium (ANSS’04). pp 55–63. (2004)Google Scholar
  5. [5]
    Project Pabadis‘Promise. www.pabadis-promise.orgGoogle Scholar
  6. [6]
    Development of Product and Production Process Description Language (PPPDL). Scholar
  7. [7]
    Gruber, T.: A Translation Approach to Portable Ontology Specifications. Knowledge Acquisition 5(2), pp. 199–220, (1993)CrossRefGoogle Scholar
  8. [8]
    Lin, H.-K., Harding, J. A., Shahbaz, M. Manufacturing System Engineering Ontology for Semantic Interoperability across Extended Project Teams. International Journal of Production Research, Vol.42, No.24, pp 5099–5118. Tylor & Francis (2004).CrossRefGoogle Scholar
  9. [9]
    Ye, Y., Yang, D., Jiang, Z., Tong, T.: Ontology-based Semantic Models for Supply Chain Management. The International Journal of Advanced Manufacturing Technology. Springer, London (2007)Google Scholar
  10. [10]
    Borgo, S., Leitao, P.: Foundations for a Core Ontology of Manufacturing. Ontologies — A Handbook of Principles, Concepts and Applications in Information Systems, Vol.14, Part 4, pp 751–775. Springer (2007)Google Scholar
  11. [11]
    Leitão, P., Colombo, A., Restivo, F.: ADACOR — A Collaborative Production Automation and Control Architecture. IEEE Intelligent Systems, Vol.20, No.1, pp 58–66. (2005)CrossRefGoogle Scholar
  12. [12]
    World Wide Web Consortium. www.w3.orgGoogle Scholar
  13. [13]
    Extensible Markup Language. Scholar
  14. [14]
    Resource Description Framework. Scholar
  15. [15]
    Resource Description Framework Schema. Scholar
  16. [16]
    Web Ontology Scholar
  17. [17]
    Semantic Web Rule Language. Scholar
  18. [18]
    Instrumentation, Systems, and Automation Society, Enterprise-Control System Integration. Parts 1,2,3. Published 2000-2005. www.isa.orgGoogle Scholar
  19. [19]
    Open Applications Group Integration Specification. www.oagi.orgGoogle Scholar
  20. [20]
    Standard for the Exchange of Product Model Data. Scholar
  21. [21]
    Machinery Information Management Information Open Systems Alliance. www.mimosa.orgGoogle Scholar
  22. [22]
    RosettaNet Standards. www.rosettanet.orgGoogle Scholar
  23. [23]
    Petroleum Industry Data Exchange (PIDX). www.pidx.orgGoogle Scholar
  24. [24]
    Industrial Automation Systems and Integration — Integration of Life-Cycle Data for Process Plants Including Oil and Gas Production Facilities.; http://15926.orgGoogle Scholar
  25. [25]
    Industrial Automation Systems and Integration — Diagnostics, Capability Assessment, and Maintenance Applications Integration Part 1. (Under Development), 2006. www.iso.orgGoogle Scholar
  26. [26]
    Function Blocks for Industrial-Process Measurement and Control Systems. www.iec.chGoogle Scholar
  27. [27]
    Studer R. et al.: Arbeitsgerechte Bereitstellung von Wissen — Ontologien für das Wissensmanagement. Technical Report, Institut AIFB, Universität Karlsruhe. 2001. Scholar
  28. [28]
    Object Management Group (OMG). www.omg.orgGoogle Scholar
  29. [29]
    Systems Modeling Language (SysML). www.sysml.orgGoogle Scholar
  30. [30]
    Unified Modeling Language (UML). www.uml.orgGoogle Scholar
  31. [31]
    XML Metadat Interchange (XMI). Scholar

Copyright information

© Springer-Verlag London Limited 2008

Authors and Affiliations

  • Markus Rabe
    • 1
  • Pavel Gocev
    • 1
  1. 1.Fraunhofer Institut Produktionsanlagen und Konstruktionstechnik (IPK)BerlinGermany

Personalised recommendations