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Application of Allen’s Temporal Logic to Ontological Modeling for Enterprise Interoperability

  • Alena V. FedotovaEmail author
  • Karl A. Hribernik
  • Klaus-Dieter Thoben
Conference paper
Part of the Proceedings of the I-ESA Conferences book series (IESACONF, volume 9)

Abstract

The problems of creating lifecycle ontologies for enterprise interoperability are addressed in this paper. Enterprise interoperability and enterprise integration are essential components of enterprise engineering (EE). A few definitions and viewpoints on EE are examined in the first hand. An original discipline for EE is considered. A generalized ontological approach to enterprise engineering is developed on the basis of combination of the lifecycle modeling, knowledge management and ontological engineering. It calls for the modeling and coordination of at least three lifecycles: enterprise lifecycle, knowledge lifecycle and product lifecycle. A general representation of lifecycle knowledge graph by a mind map is given. Particular emphasis is put on granular lifecycle upper ontology and meta-ontology. The lifecycle representations being discussed include both visualized and abstract ones. Allen’s logic is used to construct principle temporal relations between stages and phases of lifecycle.

Keywords

Enterprise interoperability Enterprise engineering Ontological modeling Product lifecycle management Allen’s logic Information granulation Fuzzy interval 

References

  1. 1.
    Panetto, H., Zdravkovich, M., Jardim-Goncalves, R., Romero, D., Cecil, J., & Mezgar, I. (2016). New perspectives for the future interoperable enterprise system. Computers in Industry. Special Issue: “Future Perspectives on Next Generation Enterprise Information System: Emerging Domains and Application Environments” 79, 47–63. Elsevier.Google Scholar
  2. 2.
    Chen, D., & Vernadat, F. (2004). Standards on enterprise integration and engineering—a state of the art. International Journal of Computer Integrated Manufacturing (IJCIM), 17(3), 235–253.CrossRefGoogle Scholar
  3. 3.
    INTEROP. (2007). Enterprise interoperability-framework and knowledge corpus—Final report, Research report of INTEROP NoE, FP6—Network of Excellence—Contract n 508011, Deliverable DI.3.Google Scholar
  4. 4.
    Liles, D., Johnson, M. E., Meade, L. M., & Ryan, D. (1995). Enterprise engineering: A discipline? In Society For Enterprise Engineering Conference Proceedings. (vol. 6).Google Scholar
  5. 5.
    Tarassov, V. B. (2002). From multi-agent systems to intelligent organizations. Moscow: Editorial URSS. (in Russian).Google Scholar
  6. 6.
    Dietz, J. (2006). Enterprise ontology—theory and methodology. Berlin: Springer.CrossRefGoogle Scholar
  7. 7.
    Dietz, J., Hoogervorst, J., et al. (2013). The Discipline of enterprise engineering. International Journal of Organisational Design and Engineering, 3(1), 86–114.CrossRefGoogle Scholar
  8. 8.
    Martin, J. (1995). The great transition: Using the seven principles of enterprise engineering to align people. New York: Technology and Strategy. American Management Association.Google Scholar
  9. 9.
    Vernadat, F. (1996). Enterprise modeling and integration: Principles and applications. London: Chapman and Hal.Google Scholar
  10. 10.
    Guryanova, M. A., Efimenko, I. V., & Khoroshevsky, V. F. (2011). Ontological modeling economy of enterprises and branches of modern Russia: Part2. In World research and development: An analytical review. Preprint WP7/ 2011/08 (part 2). State University Higher School of Economics, Moscow.Google Scholar
  11. 11.
    TOVE ontology project, http://www.eil.utoronto.ca/enterprise-modelling/tove/, last accessed 2017/11/30.
  12. 12.
    Uschold, M., King, M., Morales, S., & Zorgios, Y. (1998). The enterprise ontology. The Knowledge Engineering Review, 1(13), 31–89.CrossRefGoogle Scholar
  13. 13.
  14. 14.
    System of Systems engineering (2008). Innovations for the twenty-first century. In M. Jamshidi (Ed.). Wiley, New York.Google Scholar
  15. 15.
    Fedotova A. V., Tarassov V. B., Mouromtsev D. I., & Davydenko I. T. (2016). Lifecycle ontologies: background and state-of-the-art. In Proceedings of the 5th International Conference on Intelligent Systems and Applications (INTELLI’2016, Barcelona, Spain, November 13–17, 2016), (pp. 76–82). IARIA XPS Press, Copenhagen.Google Scholar
  16. 16.
    Kimura, F., & Suzuki, H. (1996). Product life cycle modeling for inverse manufacturing. In F. L. Krause & H. Hansen (Eds.), Proceedings of IFIP WG 5.3 International Conference on Life Cycle Modeling for Innovative Products and Processes (PROLAMAT’95, November 29-December 1, 1995). (pp. 81–89). Berlin: Springer.Google Scholar
  17. 17.
    Saaksvuory, A., & Immonen, A. (2008). Product lifecycle management. Berlin: Springer.CrossRefGoogle Scholar
  18. 18.
    Stark, J. (2011). Product lifecycle management: 21st century paradigm for product realization (2nd ed.). London: Springer.CrossRefGoogle Scholar
  19. 19.
    Jun, H.-B., Kiritsis, D., & Xirouchakis, P. (2007). Research issues on closed-loop PLM. Computers in Industry, 58, 855–868.CrossRefGoogle Scholar
  20. 20.
    Kadiria, S., Grabotb, B., Thoben, K.-D., Hribernik, K., Emmanouilidise, C., Cieminski, G., et al. (2016). Current trends on ICT technologies for enterprise information systems. Computers in Industry, 79, 14–33.CrossRefGoogle Scholar
  21. 21.
    Camarinha-Matos, L. M., & Afsarmanesh, H. (2007). A comprehensive modeling framework for collaborative networked organization. Journal of Intelligent Manufacturing, 18, 529–542.CrossRefGoogle Scholar
  22. 22.
    Tarassov, V. B. (2001). Special session on intelligent agents and virtual organizations in enterprise. In Z. Binder (Ed.), Proceedings of the 2nd IFAC/IFIP/IEEE Conference on Management and Control of Production and Logistics 2000 (MCPL’2000, Grenoble, France, July 5–8, 2000). (vol. 2, pp. 475–478). Amsterdam: Elsevier Science Publishers.Google Scholar
  23. 23.
    Tarassov, V. B., Kashuba, L. A., & Cherepanov, N. V. (1994). Concurrent engineering and AI methodologies: Opening new frontiers. In Proceedings of the IFIP International Conference on Feature Modeling and Recognition in Advanced CAD/CAM Systems (Valenciennes, France, May 1994), (Vol. 2, pp. 869–888).Google Scholar
  24. 24.
    Mal’tsev, A. I. (1973). Algebraic Systems. Berlin: Springer.CrossRefGoogle Scholar
  25. 25.
    Hriberni, K., Cassina, J., Rostad, C.C., Thoben, K.-D., & Taisch, M. (2012). Potentials of Item-level PLM and Servitization in the Leisure Boat Sector. In Proceedings of the 5th International Conference on Through-life Engineering Services (TESConf 2012).Google Scholar
  26. 26.
    Matsokis, A., & Kiritsis, D. (2010). An ontology-based approach for product lifecycle management. Computers in Industry, 61, 787–797.CrossRefGoogle Scholar
  27. 27.
    Tarassov, V. B., Fedotova, A. V., Stark, R., & Karabekov, B. S. (2015). Granular meta-ontology and extended allen’s logic: Some theoretical background and application to intelligent product lifecycle management systems. In Proceedings of the 4th International Conference on Intelligent Systems and Applications (INTELLI’2015, St.Julians, Malta, October 11–16, 2015), (pp. 86–93). IARIA XPS Press, Copenhagen (2015). ISBN: 978-1-61208-437-4.Google Scholar
  28. 28.
    Zadeh, L. A. (1997). Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets and Systems, 90, 111–127.MathSciNetCrossRefGoogle Scholar
  29. 29.
    Allen, J. F. (1983). Maintaining knowledge about temporal intervals. Communications of the ACM, 26, 832–843.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Alena V. Fedotova
    • 1
    Email author
  • Karl A. Hribernik
    • 2
  • Klaus-Dieter Thoben
    • 2
  1. 1.Bauman Moscow State Technical UniversityMoscowRussia
  2. 2.BIBA—Bremer Institut für Produktion und Logistik GmbHBremenGermany

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