Using Graph Pattern Association Rules on Yago Knowledge Base
Wahyudi Wahyudi, Masayu Leylia Khodra, Ary Setijadi Prihatmanto and 1 more
Journal of ICT Research and Applications · 2019
doi:10.5614/itbj.ict.res.appl.2019.13.2.6 (opens in a new tab)
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Abstract
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.
Abstract licence: CC BY-ND
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