Clustering Ontology-Based Metadata in the Semantic Web

  • Alexander Maedche
  • Valentin Zacharias
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2431)


The Semantic Web is an extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in cooperation. Recently, different applications based on this vision have been designed, e.g. in the fields of knowledge management, community web portals, e-learning, multimedia retrieval, etc. It is obvious that the complex metadata descriptions generated on the basis of pre-defined ontologies serve as perfect input data for machine learning techniques. In this paper we propose an approach for clustering ontology-based metadata. Main contributions of this paper are the definition of a set of similarity measures for comparing ontology-based metadata and an application study using these measures within a hierarchical clustering algorithm.


Infant Mortality Hierarchical Cluster Algorithm Multimedia Retrieval Concept Match Country Cluster 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Alexander Maedche
    • 1
  • Valentin Zacharias
    • 1
  1. 1.Research Group WIMFZI Research Center for Information Technologies at the University of KarlsruheKarlsruheGermany

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