Mining RDF metadata for generalized association rules

• Main Authors: JIANG, Tao, TAN, Ah-hwee
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2006
• Subjects: Association Rule; Resource Description Framework; Terrorist Group; Resource Description Framework Data; Root Closure; Theory and Algorithms
• Online Access: https://ink.library.smu.edu.sg/sis_research/6574; https://ink.library.smu.edu.sg/context/sis_research/article/7577/viewcontent/Jiang_Tan2006_Chapter_MiningRDFMetadataForGeneralize.pdf
• Institution: Singapore Management University

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.