Column-Oriented Datalog Materialization for Large Knowledge Graphs

Jacopo UrbaniJacopo Urbani, Ceriel JacobsCeriel Jacobs, Markus KrötzschMarkus Krötzsch

Download

Jacopo Urbani, Ceriel Jacobs, Markus Krötzsch
Column-Oriented Datalog Materialization for Large Knowledge Graphs
In Dale Schuurmans, Michael P. Wellman, eds., Proceedings of the 30th AAAI Conference on Artificial Intelligence, 258-264, 2016. AAAI Press

Details
Bibtex

KurzfassungAbstract
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.
Weitere Informationen unter:Further Information: Link
Projekt:Project: DIAMOND, HAEC B08
Forschungsgruppe:Research Group: Wissensbasierte SystemeKnowledge-Based Systems

@inproceedings{UJK2016,
  author    = {Jacopo Urbani and Ceriel Jacobs and Markus Kr{\"{o}}tzsch},
  title     = {Column-Oriented Datalog Materialization for Large Knowledge
               Graphs},
  editor    = {Dale Schuurmans and Michael P. Wellman},
  booktitle = {Proceedings of the 30th {AAAI} Conference on Artificial
               Intelligence},
  publisher = {AAAI Press},
  year      = {2016},
  pages     = {258-264}
}