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Ontology-Based Fragmented Company Knowledge Integration: Possible Approaches

  • Alexander Smirnov
  • Nikolay ShilovEmail author
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 339)

Abstract

Companies have multiple business process, some of which are supported by knowledge described via ontologies. However, due to their nature, the processes use different knowledge notation what causes a problem of integrating such fragmented heterogeneous knowledge. The paper investigates the problem of developing a single multi-domain ontology for integrating company knowledge taking into account differences between terminologies and formalisms used in various business processes. Different options of designing ontologies covering multiple domains are considered. Three of them: (i) ontology localization/multilingual ontologies, (ii) granular ontologies, and (iii) ontologies with temporal logics are considered in details and analyzed.

Keywords

Knowledge management Interoperability Multi-domain ontology 

Notes

Acknowledgements

The paper is partially due to collaboration between SPIIRAS and Festo AG & Co KG, State Research # 0073-2018-0002, projects funded by grants ## 18-07-01201, 18-07-01272 of the Russian Foundation for Basic Research. The work has been also partially financially supported by the Government of Russian Federation, Grant 08-08.

References

  1. 1.
    Yang, D., Miao, R., Wu, H., Zhou, Y.: Product configuration knowledge modeling using ontology web language. Expert Syst. Appl. 36, 4399–4411 (2009).  https://doi.org/10.1016/j.eswa.2008.05.026CrossRefGoogle Scholar
  2. 2.
    Rachuri, S., Subrahmanian, E., Bouras, A., et al.: Information sharing and exchange in the context of product lifecycle management: role of standards. Comput. Des. 40, 789–800 (2008).  https://doi.org/10.1016/j.cad.2007.06.012CrossRefGoogle Scholar
  3. 3.
    Smirnov, A.V., Shilov, N., Oroszi, A., et al.: Changing information management for product-service system engineering: customer-oriented strategies and lessons learned. Int. J. Prod. Lifecycle Manag. 11(1), 1–18 (2018).  https://doi.org/10.1504/IJPLM.2018.091647CrossRefGoogle Scholar
  4. 4.
    Oroszi, A., Jung, T., Smirnov, A., et al.: Ontology-driven codification for discrete and modular products. Int. J. Prod. Dev. 8, 162–177 (2009).  https://doi.org/10.1504/IJPD.2009.024186CrossRefGoogle Scholar
  5. 5.
    Gruber, T.R.: A translation approach to portable ontology specifications. Knowl. Acquisition 5, 199–220 (1993).  https://doi.org/10.1006/knac.1993.1008CrossRefGoogle Scholar
  6. 6.
    Staab, S., Studer, R.: Handbook on Ontologies. Springer, Heidelberg (2009).  https://doi.org/10.1007/978-3-540-92673-3CrossRefGoogle Scholar
  7. 7.
    Lafleur, M., Terkaj, W., Belkadi, F., Urgo, M., Bernard, A., Colledani, M.: An onto-based interoperability framework for the connection of PLM and production capability tools. In: Harik, R., Rivest, L., Bernard, A., Eynard, B., Bouras, A. (eds.) PLM 2016. IAICT, vol. 492, pp. 134–145. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-54660-5_13CrossRefGoogle Scholar
  8. 8.
    Smirnov, A., Shilov, N.: Ontology matching in collaborative recommendation system for PLM. Int. J. Prod. Lifecycle Manag. 6, 322–338 (2013).  https://doi.org/10.1504/IJPLM.2013.063210CrossRefGoogle Scholar
  9. 9.
    Felfernig, A., Friedrich, G., Jannach, D., et al.: Configuration knowledge representations for semantic web applications. Artif. Intell. Eng. Des. Anal. Manuf. 17, 31–50 (2003).  https://doi.org/10.1017/S0890060403171041CrossRefGoogle Scholar
  10. 10.
    Liao, Y., Lezoche, M., Panetto, H., Boudjlida, N.: Semantic annotations for semantic interoperability in a product lifecycle management context. Int. J. Prod. Res. 54, 5534–5553 (2016).  https://doi.org/10.1080/00207543.2016.1165875CrossRefGoogle Scholar
  11. 11.
    Lim, S.C.J., Liu, Y., Lee, W.B.: A methodology for building a semantically annotated multi-faceted ontology for product family modelling. Adv. Eng. Inform. 25, 147–161 (2011).  https://doi.org/10.1016/j.aei.2010.07.005CrossRefGoogle Scholar
  12. 12.
    Espinoza, M., Montiel-Ponsoda, E., Gómez-Pérez, A.: Ontology localization. In: Proceedings of the Fifth International Conference on Knowledge Capture - K-CAP 2009, New York, New York, USA, pp. 33–40. ACM Press (2009)Google Scholar
  13. 13.
    Zadeh, L.A.: Is there a need for fuzzy logic? Inf. Sci. (Ny) 178, 2751–2779 (2008).  https://doi.org/10.1016/j.ins.2008.02.012CrossRefGoogle Scholar
  14. 14.
    Calegari, S., Ciucci, D.: Granular computing applied to ontologies. Int. J. Approximate Reasoning 51, 391–409 (2010).  https://doi.org/10.1016/j.ijar.2009.11.006CrossRefGoogle Scholar
  15. 15.
    Jankowski, A., Skowron, A.: Toward rough-granular computing. In: An, A., Stefanowski, J., Ramanna, S., Butz, C.J., Pedrycz, W., Wang, G. (eds.) RSFDGrC 2007. LNCS (LNAI), vol. 4482, pp. 1–12. Springer, Heidelberg (2007).  https://doi.org/10.1007/978-3-540-72530-5_1CrossRefGoogle Scholar
  16. 16.
    Inuiguchi, M., Hirano, S., Tsumoto, S.: Rough Set Theory and Granular Computing. Springer, Heidelberg (2003).  https://doi.org/10.1007/978-3-540-36473-3CrossRefGoogle Scholar
  17. 17.
    Polkowski, L., Skowron, A.: Rough mereological calculi of granules: a rough set approach to computation. Comput. Intell. 17, 472–492 (2001).  https://doi.org/10.1111/0824-7935.00159CrossRefGoogle Scholar
  18. 18.
    Tarassov, V., Fedotova, A., Stark, R., Karabekov, B.: Granular meta-ontology and extended allen’s logic: some theoretical background and application to intelligent product lifecycle management systems valery. In: Schwab, I., van Moergestel, L., Gonçalves, G. (eds) The Fourth International Conference on Intelligent Systems and Applications, INTELLI 2015, St. Julians, Malta, pp. 86–93 (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.ITMO UniversitySt. PetersburgRussia
  2. 2.SPIIRASSt. PetersburgRussia

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