摘要：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 rulegenerated
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.
其他摘要：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 1 114 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.
