Column-Oriented Datalog Materialization for Large Knowledge Graphs

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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)

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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.


Topics

Rule languages