GDL

Journal from gdlhub / 2019-11-16 15:06:12
Oleh : Wahyudi Wahyudi, Masayu Leylia Khodra, Ary Setijadi Prihatmanto, Carmadi Machbub, ITB
Dibuat : 2019-11-16, dengan 1 file

Keyword : association rule, graph pattern, knowledge base, PCA confidence, standard confidence
Url : http://journals.itb.ac.id/index.php/jictra/article/view/7388
Sumber pengambilan dokumen : WEB

The use of graph pattern association rules (GPARs) on the Yago knowledge base is proposed. Extending association rules for itemsets, GPARS can help to discover regularities between entities in a knowledge base. A rule-generated graph pattern (RGGP) algorithm was used for extracting rules from the Yago knowledge base and a GPAR algorithm for creating the association rules. Our research resulted in 1114 association rules, with the value of standard confidence at 50.18% 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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