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

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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.

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