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.
Cite
Text
Urbani et al. "Column-Oriented Datalog Materialization for Large Knowledge Graphs." AAAI Conference on Artificial Intelligence, 2016. doi:10.1609/AAAI.V30I1.9993Markdown
[Urbani et al. "Column-Oriented Datalog Materialization for Large Knowledge Graphs." AAAI Conference on Artificial Intelligence, 2016.](https://mlanthology.org/aaai/2016/urbani2016aaai-column/) doi:10.1609/AAAI.V30I1.9993BibTeX
@inproceedings{urbani2016aaai-column,
title = {{Column-Oriented Datalog Materialization for Large Knowledge Graphs}},
author = {Urbani, Jacopo and Jacobs, Ceriel J. H. and Krötzsch, Markus},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2016},
pages = {258-264},
doi = {10.1609/AAAI.V30I1.9993},
url = {https://mlanthology.org/aaai/2016/urbani2016aaai-column/}
}