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Semantic Knowledge Discovery from Heterogeneous Data Sources

  • Claudia d’Amato
  • Volha Bryl
  • Luciano Serafini
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7603)

Abstract

Available domain ontologies are increasing over the time. However there is a huge amount of data stored and managed with RDBMS. We propose a method for learning association rules from both sources of knowledge in an integrated way. The extracted patterns can be used for performing: data analysis, knowledge completion, ontology refinement.

Keywords

Association Rule Frequent Itemsets Mining Association Rule Domain Ontology Support Threshold 
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 2012

Authors and Affiliations

  • Claudia d’Amato
    • 1
  • Volha Bryl
    • 2
  • Luciano Serafini
    • 2
  1. 1.Department of Computer ScienceUniversity of BariItaly
  2. 2.Data & Knowledge Management Unit - Fondazione Bruno KesslerItaly

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