| Skip to main content | Skip to navigation |

Mining RDF Metadata for Generalized Association Rules: Knowledge Discovery in the Semantic Web Era

  • Tao Jiang, Nanyang Technological University, Singapore
  • Ah-Hwee Tan, Nanyang Technological University, Singapore

Full text:

Track: Posters

Resource Description Framework (RDF) is a specification proposed by the World Wide Web Consortium (W3C) for describing and interchanging semantic metadata on the Semantic Web. Due to the continual popularity of the Semantic Web, in a foreseeable future, there will be a sizeable amount of RDF-based content available on the web, offering tremendous opportunities in discovering useful knowledge from large RDF databases. 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 datasets show that our proposed method can substantially reduce the pattern redundancy and perform much faster than Cumulate, the original generalized association rule mining algorithm.

Other items being presented by these speakers

Organised by

ECS Logo

in association with

BCS Logo ACM Logo

Platinum Sponsors

Sponsor of The CIO Dinner

Valid XHTML 1.0! IFIP logo WWW Conference Committee logo Web Consortium logo Valid CSS!