Advertisement

Knowledge Management Model Based on the Enterprise Ontology for the KB DSS System of Enterprise Situation Assessment in the SME Sector

Conference paper
  • 1.2k Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 787)

Abstract

In the paper, the knowledge management model based on the original idea of the enterprise ontology is presented. This model is the basis of construction of the Knowledge Based Decision Support System (KB DSS) for evaluation of situation of enterprises in the SME sector. In the model, the SECI model of knowledge creation proposed by I. Nonaka and H. Takeuchi is applied. The model consists of a cycle of creating evaluation of situation of enterprises in the potential-risk space of activity. To design the enterprise ontology, ideas of Polish philosophers (J. Bochenski and R. Ingarden) are applied. Taxonomies of classes of the enterprise potential and risk are presented in the OWL language (the Protege editor). The KB DSS architecture is consistent with the Case Based Reasoning (CBR) methodology.

Keywords

Enterprise ontology KB DSS system CBR methodology SECI model of knowledge creation 

References

  1. 1.
    Zarate, P., Liu, S.: A new trend for knowledge-based decision support systems design. Int. J. Inf. Decis. Sci. 8(3), 305–324 (2016)Google Scholar
  2. 2.
    Nonaka, I., Takeuchi, H.: The Knowledge-Creating Company. How Japanese Companies Create the Dynamics of Innovation. Oxford University Press, New York, Oxford (1995)Google Scholar
  3. 3.
    Bandera, C., Keshtkar, F., Bartolacci, M.R., Neerudu, S., Passerini, K.: Knowledge management and the entrepreneur: insights from Ikujiro Nonaka’s Dynamic Knowledge Creation model (SECI). Int. J. Innov. Stud. 1, 1163–1174 (2017)CrossRefGoogle Scholar
  4. 4.
    Figueiredo, M.S.N., Pereira, A.M.: Managing knowledge – the importance of databases in the scientific production. Proc. Manuf. 12, 166–173 (2017)Google Scholar
  5. 5.
    Ale, M.A., Toledo, C.M., Chiotti, O., Galli, M.R.: A conceptual model and technological support for organizational knowledge management. Sci. Comput. Program. 95, 73–92 (2014)CrossRefGoogle Scholar
  6. 6.
    Bocheński, J.M.: Przyczynek do filozofii przedsiębiorstwa przemysłowego. Logika i filozofia. Wybór pism. Warszawa PWN, pp. 162–186 (1993)Google Scholar
  7. 7.
    Wand, Y., Weber, R.: An ontological model of an information system. IEEE Trans. Softw. Eng. 16, 1282–1292 (1990)CrossRefGoogle Scholar
  8. 8.
    Milton, S.K., Kazmierczak, E.: An ontology of data modeling languages: a study using a common-sense realistic ontology. J. Database Manag. 15(2), 19–38 (2004)CrossRefGoogle Scholar
  9. 9.
    Dietz, J.L.G.: Enterprise Ontology. Theory and Methodology. Springer, Heidelberger (2006)CrossRefGoogle Scholar
  10. 10.
    Arp, R., Smith, B., Spear, D.: Building Ontologies with Basic Formal Ontology. Massachusetts Institute of Technology, Cambridge (2015)CrossRefGoogle Scholar
  11. 11.
    Saaty, T.L.: Decision Making for Leaders. RWS Publications, Pittsburgh (2001)Google Scholar
  12. 12.
    Mousseau, V., Słowiński, R., Zielniewicz, P.: A user-oriented implementation of the ELECTRE TRI method integrating preference elicitation support. Comput. Oper. Res. 27, 757–777 (2000)CrossRefGoogle Scholar
  13. 13.
    Tavana, M.: Euclid: strategic alternative assessment matrix. J. Multi-Criteria Decis. Anal. 11, 75–96 (2002)CrossRefGoogle Scholar
  14. 14.
    Argenti, J.: Corporate Collapse: The Causes and Symptoms. McGraw-Hill, London (1976)Google Scholar
  15. 15.
    Aamodt, A., Plaza, E.: Case-based reasoning: foundations issues, methodological variations and system approaches. AI Commun. 7(1), 39–59 (1994)Google Scholar
  16. 16.
    Ingarden, R.: Spór o istnienie świata. PWN, Warszawa (1987)Google Scholar
  17. 17.
    Kaplan, R.S., Norton, D.P.: Balanced Scorecard. Translating Strategy into Action. Harvard Business School Press, Boston (1996)Google Scholar
  18. 18.
    Richardson, B., Nwankwo, S., Richardson, S.: Understanding the causes of business failure crises: generic failure types: boiled frogs, drowned frogs, bullfrogs and tadpoles. Manag. Decis. 32(4), 9–22 (1994)CrossRefGoogle Scholar
  19. 19.
    Hitzler, P., Krotzsch, M., Rudolph, S.: Foundations of Semantic Web Technologies. Chapman & Hall/CRC, Boca Raton (2010)Google Scholar
  20. 20.
    OWL 2 Web Ontology Language Document Overview, 2nd edn. https://www.w3.org/TR/2012/REC-owl2-overview-20121211/
  21. 21.
  22. 22.
  23. 23.
    Giovannini, A., Aubry, A., Panetto, H., Dassisti, M., Haouzi, H.: Ontology-based system for supporting manufacturing sustainability. Ann. Rev. Control 36, 309–317 (2012)CrossRefGoogle Scholar
  24. 24.
    Middleton, S.E., Roure, D.D., Shadbolt, N.R.: Ontology-based recommender system. In: Stab, S., Studer, R. (eds.) Handbook on Ontologies, International Handbooks on Information Systems, pp. 779–795. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  25. 25.
  26. 26.
    Sauer, S.: Knowledge elicitation and formalisation for context and explanation-aware computing with case-based recommender system. Doctoral thesis. University of West London (2016). https://repository.uwl.ac.uk/id/eprint/2226/
  27. 27.
    Recio-Garcia, J.A., Gonzalez-Calero, P.A., Diaz-Agudo, B.: jCOLIBRI: a framework for building case-based reasoning systems. Sci. Comput. Program. 79, 126–145 (2014)CrossRefGoogle Scholar
  28. 28.
  29. 29.
    Andreasik, J.: A case-based reasoning system for predicting the economic situation of enterprises – tacit knowledge capture process (externalization). In: Kurzyński, M., et al. (eds.) Computer Recognition Systems 2. Advances in Soft Computing, ASC, vol. 45, pp. 718–730. Springer, Heidelberg (2007)Google Scholar
  30. 30.
    Andreasik, J.: The knowledge generation about an enterprise in the KBS-AE (knowledge-based system – acts of explanation). In: Nguyen, N.T., et al. (eds.) New Challenges in Computational Collective Intelligence. Studies in Computational Intelligence, SCI, vol. 244, pp. 85–94. Springer, Heidelberg (2009)Google Scholar
  31. 31.
    Andreasik, J.: Decision support system for assessment of enterprise competence. In: Kurzynski, M., Wozniak, M. (eds.) Computer Recognition Systems 3. Advances in Intelligent and Soft Computing, AISC, vol. 57, pp. 559–567. Springer, Heidelberg (2009)Google Scholar
  32. 32.
    Andreasik J.: Enterprise ontology according to Roman Ingarden formal ontology. In: Cyran, K.A., et al. (eds.) Man-Machine Interactions. Advances in Intelligent and Soft Computing, AISC, vol. 59, pp. 85–94. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  33. 33.
    Andreasik, J.: Enterprise ontology for knowledge-based system. In: Hippe, Z.S., Kulikowski, J.L. (eds.) Human-Computer System Interactions. Advances in Intelligent and Soft Computing, AISC, vol. 60, pp. 443–458. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  34. 34.
    Andreasik, J.: Enterprise ontology-diagnostic approach. In: Proceedings of the Conference on Human System Interactions, HSI 2008, Book Series: Eurographics Technical Report Series, Krakow, Poland, pp. 503–509. IEEE (2008).  https://doi.org/10.1109/hsi.2008.4581489
  35. 35.
    Andreasik, J.: Ontology of offers according to Ingarden’s theory of individual objects. In: Proceedings of the 5th International Conference on Agents and Artificial Intelligence, ICAART 2013, pp. 429–432. SciTePress (2013).  https://doi.org/10.5220/0004209304290432

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.University of Information Technology and ManagementRzeszówPoland

Personalised recommendations