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.
@article{arxiv.1511.08915,
title = {Column-Oriented Datalog Materialization for Large Knowledge Graphs (Extended Technical Report)},
author = {Jacopo Urbani and Ceriel Jacobs and Markus Krötzsch},
journal= {arXiv preprint arXiv:1511.08915},
year = {2016}
}