OTO Model of Building of Structural Knowledge – Areas of Usage and Problems

  • Krzysztof Wójcik
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 184)

Summary

This article describes an OTO (Observation-Transformation-Operation) model which allows to improve building of the knowledge structure of the simple agent systems. The presented approach tries to overcome the crucial problems of the task of the automatic ontology building. To this end inductive learning methods and knowledge transformations are utilized. The article provides a brief outline of various forms of these transformations. The chosen example of their usage in building of the partial knowledge structure is also presented. As a conclusion, the paper points to the many possible areas of the model usage, mainly in the field of the image processing and image understanding.

Keywords

Logic Programming Knowledge Structure Concept Structure Knowledge Building Central Object 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Davies, J., Studer, R., Warren, P.: Semantic Web Technologies Trends and Research in Ontology-based Systems. John Wiley & Sons Ltd. (2006)Google Scholar
  2. 2.
    Gruszczyk-Kolczyńska, E., Zielińska, E.: Dziecięca matematyka. Edukacja matematyczna dzieci w domu, w przedszkolu i szkole. WSiP Warszawa (1997) (in Polish)Google Scholar
  3. 3.
    Mitchell, T.M.: Machine Learning. McGraw-Hill Science (1997)Google Scholar
  4. 4.
    Muggleton, S.H., De Raedt, L.: Inductive logic programming: Theory and methods. Journal of Logic Programming 19/20 (1994)Google Scholar
  5. 5.
    Piekarczyk, M., Ogiela, M.R.: Hierarchical Graph-Grammar Model for Secure and Efficient Handwritten Signatures Classification. Journal of Universal Computer Science 17 (2011)Google Scholar
  6. 6.
    Tadeusiewicz, R., Ogiela, M.R.: Medical Image Understanding Technology. STUDFUZZ, vol. 156. Springer, Heidelberg (2004)Google Scholar
  7. 7.
    Wójcik, K.: Inductive Learning Methods in the Simple Image Understanding System. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds.) ICCVG 2010, Part I. LNCS, vol. 6374, pp. 97–104. Springer, Heidelberg (2010)Google Scholar
  8. 8.
    Wójcik, K.: Hierarchical Knowledge Structure Applied to Image Analyzing System - Possibilities of Practical Usage. In: Tjoa, A.M., Quirchmayr, G., You, I., Xu, L. (eds.) ARES 2011. LNCS, vol. 6908, pp. 149–163. Springer, Heidelberg (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Krzysztof Wójcik
    • 1
  1. 1.Institute of Computer SciencePedagogical University of CracowKrakówPoland

Personalised recommendations