The evaluation of Datalog rules over large Knowledge Graphs (KGs) is essential for many applications. In this paper, we present a new method of materializing Datalog inferences, which combines a column-based memory layout with novel optimization methods that avoid redundant inferences at runtime. The pro-active caching of certain subqueries further increases efficiency. Our empirical evaluation shows that this approach can often match or even surpass the performance of state-of-the-art systems, especially under restricted resources.
|Title of host publication||30th AAAI Conference on Artificial Intelligence, AAAI 2016|
|Number of pages||7|
|Publication status||Published - 2016|
|Event||30th AAAI Conference on Artificial Intelligence, AAAI 2016 - Phoenix, United States|
Duration: 12 Feb 2016 → 17 Feb 2016
|Conference||30th AAAI Conference on Artificial Intelligence, AAAI 2016|
|Period||12/02/16 → 17/02/16|