Name
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 |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Databases | Data mining,Graph,Computer science,Association rule learning,Knowledge base,Completeness (statistics),Computation |
DocType | Volume | Citations |
Journal | abs/1810.00326 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wahyudi | 1 | 0 | 0.68 |
| Masayu Leylia Khodra | 2 | 1 | 2.88 |
| Ary Setijadi Prihatmanto | 3 | 0 | 3.72 |
| Machbub, Carmadi | 4 | 0 | 5.07 |