Automatic Representation of Semantic Abstraction of Geographical Data by Means of Classification

  • Rainer Larin Fonseca
  • Eduardo Garea Llano
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6419)


Providing Geographical Information Systems (GIS) with the mechanisms for processing geographical data based on their semantic abstraction is a task that at present is carried out in a number of research given their scope of applications. Tackling this issue may help to solve many problems of geographical data like its heterogeneity, since the SIG could process geographical data focusing on their meaning and not on their syntax and/or structure, thus reducing the Man-Machine semantic gap. An important aspect for achieving these objectives is the establishment of an automatic way of correspondence between geographical data and their conceptualization in a Domain Ontology. In this work, we propose a new type of Ontology, a Data-Representation Ontology. We also propose a new method for the automatic generation of the Data-Representation Ontology from geographical data and his interrelationships with the Domain Ontology. For this we use pattern classification techniques and a dissimilarity measure. The experiments showed that once the Data-Representation Ontology was generated, the classifier using dissimilarities could correctly classify all the data.


Ontology Classification Semantic Geographical data 


  1. 1.
    Leung, Y.: Knowledge Discovery in Spatial Data. Springer, Heidelberg (2010)Google Scholar
  2. 2.
    Visser, U.: Intelligent Information Integration for the Semantic Web. LNCS. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  3. 3.
    Kavouras, M., Kokla, M., Tomai, E.: Comparing categories among geographic ontologies. In: Computers & Geosciences, Special Issue, Geospatial Research in Europe: AGILE 2003 (2003)Google Scholar
  4. 4.
    Schwering, A., Raubal, M.: Spatial relations for semantic similarity measurement. In: Akoka, J., Liddle, S.W., Song, I.-Y., Bertolotto, M., Comyn-Wattiau, I., van den Heuvel, W.-J., Kolp, M., Trujillo, J., Kop, C., Mayr, H.C. (eds.) ER Workshops 2005. LNCS, vol. 3770, pp. 259–269. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  5. 5.
    Hakimpour, F.: Using Ontologies to Resolve Semantic Heterogeneity for Integrating Spatial Database Schemata. PhD thesis Zurich University (2003)Google Scholar
  6. 6.
    Hess, G.N., Iochpe, C.: Ontology-driven resolution of semantic heterogeneities in gdb conceptual schemas. In: Proceedings of the GEOINFO 2004: VI Brazilian Symposium on GeoInformatics (2004)Google Scholar
  7. 7.
    Fonseca, F.T.: Ontology-Driven Geographic Information Systems, The University of Maine (2001)Google Scholar
  8. 8.
    ESDIG. Diccionario del Espacio Digital Geografico ESDIG (2010), (cited 2010 Enero)
  9. 9.
    Pekalska, E., Duin, R.P.W.: The dissimilarity representation for pattern recognition. Foundations and Applications 64 (2005)Google Scholar
  10. 10.
    Lehmann, F.: Semantic networks. Computers Math. Applic. 23, 1–50 (1992)CrossRefzbMATHGoogle Scholar
  11. 11.
    Minsky, M.: A framework for representing knowledge. In: Winston, P.H. (ed.) The Psychology of Computer Vision. McGraw-Hill, New York (1975)Google Scholar
  12. 12.
    Gruber, T.: Ontolingua: A mechanism to support portable ontologies. Stanford University, Stanford (1992)Google Scholar
  13. 13.
    Studer, S., Benjamins, R., Fensel, D.: Knowledge Engineering: Principles and Methods. Data and Knowledge Engineering (1998)Google Scholar
  14. 14.
    Guarino, N.: Formal Ontology and Information Systems. In: Proceedings of FOIS 1998. National Research Council, LADSEB–CNR (1998)Google Scholar
  15. 15.
    Fix, E., Hodges, J.L.: Discriminatory analysis, nonparametric discrimination: Consistency properties. Technical Report 4, USAF School of Aviation Medicine, Randolph Field, Texas (1951)Google Scholar
  16. 16.
    Backhaus, K., et al.: Multivariate analysis methods. In: An application-oriented introduction. Springer, Berlin (2000)Google Scholar
  17. 17.
    IDERC: Infraestructura de Datos Espaciales de la República de Cuba (2010), (cited 2010 Marzo)
  18. 18.
    ESRI: ESRI Home Page (2010), (cited 2010 Enero)
  19. 19.
    Egenhhofer, M.J.: A model for detailed binary topological relationships. Geomatica 47(3&4) (1993)Google Scholar
  20. 20.
    Larin-Fonseca, R., Garea-Llano, E.: Topological Relations as Rule for Automatic Generation of Geospatial Application Ontology. In: Proceedings of VII Jornadas para el Desarrollo de Grandes Aplicaciones de Red (2010) (in press)Google Scholar
  21. 21.
    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),
  22. 22.
    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),

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

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

Personalised recommendations