Mining RDF Metadata for Generalized Association Rules

  • Tao Jiang
  • Ah-Hwee Tan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4080)


In this paper, we present a novel frequent generalized pattern mining algorithm, called GP-Close, for mining generalized associations from RDF metadata. To solve the over-generalization problem encountered by existing methods, GP-Close employs the notion of generalization closure for systematic over-generalization reduction. Empirical experiments conducted on real world RDF data sets show that our method can substantially reduce pattern redundancy and perform much better than the original generalized association rule mining algorithm Cumulate in term of time efficiency.


Association Rule Resource Description Framework Terrorist Group Resource Description Framework Data Root Closure 
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 2006

Authors and Affiliations

  • Tao Jiang
    • 1
  • Ah-Hwee Tan
    • 1
  1. 1.School of Computer EngineeringNanyang Technological UniversitySingapore

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