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)

Abstract

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.

Keywords

Ontology Classification Semantic Geographical data 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

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

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