CSR: Discovering Subsumption Relations for the Alignment of Ontologies

  • Vassilis Spiliopoulos
  • Alexandros G. Valarakos
  • George A. Vouros
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5021)


For the effective alignment of ontologies, the computation of equivalence relations between elements of ontologies is not enough: Subsumption relations play a crucial role as well. In this paper we propose the "Classification-Based Learning of Subsumption Relations for the Alignment of Ontologies" (CSR) method. Given a pair of concepts from two ontologies, the objective of CSR is to identify patterns of concepts’ features that provide evidence for the subsumption relation among them. This is achieved by means of a classification task, using state of the art supervised machine learning methods. The paper describes thoroughly the method, provides experimental results over an extended version of benchmarking series and discusses the potential of the method.


ontology alignment subsumption supervised machine learning 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Vassilis Spiliopoulos
    • 1
    • 2
  • Alexandros G. Valarakos
    • 1
  • George A. Vouros
    • 1
  1. 1.AI Lab, Information and Communication Systems Engineering DepartmentUniversity of the AegeanSamosGreece
  2. 2.Institution of Informatics and TelecommunicationsNCSR ”Demokritos”Greece

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