Name
Abstract | ||
|---|---|---|
Many Web applications require efficient querying of large Knowledge Graphs (KGs). We propose KOGNAC, a dictionary-encoding algorithm designed to improve SPARQL querying with a judicious combination of statistical and semantic techniques. In KOGNAC, frequent terms are detected with a frequency approximation algorithm and encoded to maximise compression. Infrequent terms are semantically grouped into ontological classes and encoded to increase data locality. We evaluated KOGNAC in combination with state-of-the-art RDF engines, and observed that it significantly improves SPARQL querying on KGs with up to 1B edges. |
Year | Venue | DocType |
|---|---|---|
2016 | IJCAI | Conference |
Volume | Citations | PageRank |
abs/1604.04795 | 4 | 0.37 |
References | Authors | |
19 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jacopo Urbani | 1 | 515 | 34.01 |
| Sourav Dutta | 2 | 25 | 6.03 |
| Sairam Gurajada | 3 | 118 | 7.83 |
| Gerhard Weikum | 4 | 12710 | 2146.01 |