Semantic Representation of Geospatial Objects Using Multiples Knowledge Domains

  • Rainer Larin Fonseca
  • Eduardo Garea Llano
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7441)


Geographical data is obtained through abstractions made from objects in the real world. Generally, each of these abstractions is obtained by taking into account only one point of view about the object being analyzed. When different abstractions are made on the same object different data sources regarding to it are produced. These data sources are generally heterogeneous. Thus the semantic processing of these objects become challenge since different data sources must be combined to obtain good results in tasks such as information retrieval and analysis for decision-making. This paper presents an approach based on ontologies to enrich the semantic representation of geospatial objects taking into account different abstractions made on them. The experimental results show the usefulness of this approach and how it is possible to make a multidimensional semantic representation automatically using classification algorithms and search techniques on trees.


Ontology Classification Semantic Representation Geospatial Data 


  1. 1.
    Gärdenfors, P.: Conceptual spaces as a framework for knowledge representation. Mind and Matter 2, 9–27 (2004)Google Scholar
  2. 2.
    Raubal, M.: Formalizing Conceptual Spaces. Formal Ontology in Information Systems. In: Proceedings of the Third International Conference (FOIS 2004), p. 114 (2004)Google Scholar
  3. 3.
    Carmagnola, F., et al.: A Semantic Framework for Adaptive web-based Systems. In: Proceedings of the 9th Conference on Advances in Artificial Intelligence, pp. 370–380 (2005)Google Scholar
  4. 4.
    Balley, S., Parent, C., Spaccapietra, S.: Modelling geographic data with multiple representations. International Journal of Geographical Information Science 18, 329–354 (2004)CrossRefGoogle Scholar
  5. 5.
    Adnani, M.E., Yétongnon, K., Benslimane, D.: A multiple layered functional data model to support multiple representations and interoperability of GIS: application to urban management systems. In: Proceedings of the 9th ACM International Symposium on Advances in Geographic Information Systems, pp. 70–75 (2001)Google Scholar
  6. 6.
    Strang, T., Linnhoff-Popien, C., Frank, K.: CoOL: A Context Ontology Language to Enable Contextual Interoperability. In: Stefani, J.-B., Demeure, I., Zhang, J. (eds.) DAIS 2003. LNCS, vol. 2893, pp. 236–247. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  7. 7.
    Ahlqvist, O.: A Parameterized Representation of Uncertain Conceptual Spaces. Transactions in GIS 8(4), 493–514 (2004)CrossRefGoogle Scholar
  8. 8.
    Larin-Fonseca, R., Garea-Llano, E.: Automatic Representation of Geographical Data from Semantic Point of View throughout a New Ontology and Classification Techniques. Transaction in GIS 15(1) (2011)Google Scholar
  9. 9.
    Jaccard, P.: Étude comparative de la distribution florale dans une portion des Alpes et des Jura. Bulletin de la Société Vaudoise des Sciences Naturelles 37, 547–579 (1901)Google Scholar
  10. 10.
    MATLAB, version 7.10.0 (R2010a). The MathWorks Inc., Natick (2010) Google Scholar
  11. 11.
    Duin, R.P.W., et al.: PRTools4 A Matlab Toolbox for Pattern Recognition Version 4.1.5. Delft Pattern Recognition Research, Faculty EWI - ICT, Delft University of Technology, The Netherlands (2009),
  12. 12.
    Duin, R.P.W., et al.: DisTools A Matlab Toolbox for Pattern Recognition Delft Pattern Recognition Research, Faculty EWI - ICT, Delft University of Technology, The Netherlands (2009),

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Rainer Larin Fonseca
    • 1
  • Eduardo Garea Llano
    • 1
  1. 1.Advanced Technologies Application CentreHavanaCuba

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