Using Graph-Pattern Association Rules On Yago Knowledge Base

Ary Setijadi Prihatmanto; Carmadi Machbub; Masayu Leylia Khodra; Wahyudi

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Using Graph-Pattern Association Rules On Yago Knowledge Base

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arxiv 1810.00326 v1 pith:DZUH3LSX submitted 2018-09-30 cs.DB

Wahyudi , Masayu Leylia Khodra , Ary Setijadi Prihatmanto , Carmadi Machbub This is my paper

classification cs.DB

keywords rulesassociationconfidenceknowledgebasegraph-patternyagoalgorithm

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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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