Column-Oriented Datalog Materialization for Large Knowledge Graphs
Jacopo Urbani, Ceriel J. H. Jacobs, Markus Krötzsch
Column-Oriented Datalog Materialization for Large Knowledge Graphs
Abstract. 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.
Published at AAAI2016 (Conference paper)
Download PDF (last update: July 2 2016)
Citation details
- Jacopo Urbani, Ceriel J. H. Jacobs, Markus Krötzsch. Column-Oriented Datalog Materialization for Large Knowledge Graphs. In Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI-16), pp. 258–264. AAAI PressProperty "Publisher" has a restricted application area and cannot be used as annotation property by a user. 2016.
author = {Jacopo Urbani and Ceriel J. H. Jacobs
and Markus Kr\"{o}tzsch},
title = {Column-Oriented Datalog Materialization for
Large Knowledge Graphs},
pages = {258--264},
booktitle = {Proceedings of the 30th AAAI Conference on
Artificial Intelligence (AAAI'16)},
publisher = {AAAI Press},
year = {2016}
}
