Using Graph-Pattern Association Rules On Yago Knowledge Base

2018/09/30 by Wahyudi, Masayu Leylia Khodra, Khodra, Masayu Leylia +4
Computer Science · #Databases (cs.DB) #FOS: Computer and information sciences #cs.DB

paper · pdf · doi:10.48550/arxiv.1810.00326

arxiv created 2018/09/30 · arxiv updated 2018/10/02

Abstract

We propose the use of Graph-Pattern Association Rules (GPARs) on the Yago knowledge base. Extending association rules for itemsets, GPARS can help to discover regularities between entities in knowledge bases. A rule-generated graph pattern (RGGP) algorithm was used for extracting rules from the Yago knowledge base and a graph-pattern association rules algorithm for creating association rules. Our research resulted in 1114 association rules, where the value of standard confidence at 50.18% was better than partial completeness assumption (PCA) confidence at 49.82%. Besides that the computation time for standard confidence was also better than for PCA confidence

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