A data granulation model for searching knowledge about diagnosed objects

  • Anna BryniarskaEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 577)


Diagnostic knowledge about chosen technical classes of objects can be effective gained by analyzing Internet webpages. In this paper for analyzing these data is proposed the data granulation method. Information granules are mathematical models describing data aggregates. Data aggregates are connected with each other and described by the Fuzzy Description Logic. It is presented that this data granulation model can be used to sharpen the diagnostic knowledge.


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  1. 1. Baader, F., Calvanese, D., McGuinness, D., Nardi, D., Patel-Schneider, P. (eds.): The Description Logic. Handbook Theory, Implementation and Application. Cambridge University Press, Cambridge (2003)Google Scholar
  2. 2. Baumeister J.: Agile Development of Diagnostic Knowledge Systems. infix, Akademische Verlagsgesellschaft Aka GmbH, Berlin (2004)Google Scholar
  3. 3. Belard N., Pencol’e Y., Combacau M.: A theory of meta-diagnosis: Reasoning about diagnostic systems. In Twenty-Second International Joint Conference on Artificial Intelligence (IJCAI11), pp. 731–737, (2011)Google Scholar
  4. 4. Bloch I.: Mathematical Morphology. In: Handbook of Spatial Logics, M. Aiello, I.Pratt-Hartmann and J. van Benthem (eds.), pp. 857–944, Springer (2007)Google Scholar
  5. 5. Bobillo, F., Straccia, U.: fuzzyDL: An expressive fuzzy description logic reasoner.In: Proc. IEEE Int. Conference on Fuzzy Systems FUZZ-IEEE 2008 (IEEEWorld Congress on Computational Intelligence), pp. 923–930, (2008)Google Scholar
  6. 6. Bryniarska, A.: The Paradox of the Fuzzy Disambiguation in the Information Retrieval. (IJARAI) International Journal of Advanced Research in Artificial Intelligence, pp. 55–58, Volume 2 No 9, (2013)Google Scholar
  7. 7. Bryniarska, A.: The Model of PossibleWeb Data Retrieval. Proceedings of 2nd IEEE International Conference on Cybernetics CYBCONF 2015, pp. 348–353, (2015)Google Scholar
  8. 8. Bryniarska, A.: An Uncertain Diagnostic System of the Constructional and Technological Preferences. Proc. Of The 21st International Conference on Methods and Models in Automation and Robotics MMAR 2016, pp. 256–260, (2016)Google Scholar
  9. 9. Fanizzi, N., d’Amato, C., Esposito, F., Lukasiewicz, T.: Representing uncertain concepts in rough description logics via contextual indiscernibility relations. In: Bobillo, F., da Costa, P.C.G., d’Amato, C., et al. (eds.) Proc. 4th Int. Workshop on Uncertainty Reasoning for the Semantic Web, (2008)Google Scholar
  10. 10. Kosko B.: Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence. Prentice Hall, Englewood Cliffs, N.J. (1992)Google Scholar
  11. 11. Pedrycz, W.: Allocation of information granularity in optimization and decision-making models: towards building the foundations of Granular Computing, pp. 137–145, (2014)Google Scholar
  12. 12. Pedrycz, W.: Granular computing: analysis and design of intelligent systems. Taylor & Francis Group, Abingdon (2013)Google Scholar
  13. 13. Simou, N., Mailis, T., Stoilos, G., Stamou, S.: Optimization techniques for fuzzy description logics. In: Description Logics. Proc. 23rd Int.Workshop on Description Logics (DL 2010). CEUR-WS, vol. 573, (2010)Google Scholar
  14. 14. Serra J.: Image Analysis and Mathematical Morphology. Academic Press (1982)Google Scholar
  15. 15. Skowron A., Swiniarski R., Synak P.: Approximation Spaces and Information Granulation. In: J.F. Peters and A. Skowron (Eds.): Transactions on Rough Sets III, LNCS 3400, pp. 175–189, Springer-Verlag Berlin Heidelberg (2005)Google Scholar
  16. 16. Yao, Y.Y. : The art of granular computing. In: Proceeding of the International Conference on Rough Sets and Emerging Intelligent Systems Paradigms LNAI 4585, pp. 101–112, (2007)Google Scholar

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© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Institute of Computer ScienceOpole University of TechnologyOpolePoland

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