# Column-Oriented Datalog Materialization for Large Knowledge Graphs (Extended Technical Report)

arXiv: https://arxiv.org/abs/1511.08915 · HTML: https://mlsanity.com/paper/1511.08915/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Nov 2015.

## Built on by (most cited first, 8 of 8)

- [Tractable Diversity: Scalable Multiperspective Ontology Management via Standpoint EL](https://mlsanity.com/paper/2302.13187/) (2023)
- [Materializing Knowledge Bases via Trigger Graphs](https://mlsanity.com/paper/2102.02753/) (2021)
- [Efficient Dependency Analysis for Rule-Based Ontologies](https://mlsanity.com/paper/2207.09669/) (2022)
- [Nemo: First Glimpse of a New Rule Engine](https://mlsanity.com/paper/2308.15897/) (2023)
- [Grounding Rule-Based Argumentation Using Datalog](https://mlsanity.com/paper/2508.10976/) (2025)
- [Efficient Temporal Datalog Materialisation for Composite Event Recognition](https://mlsanity.com/paper/2605.02488/) (2026)
- [Stratified Negation in RDF Rules: A Correct Approach (Extended Version)](https://mlsanity.com/paper/2607.28778/) (2026)
- [Recovering Explanations from Transformed Rule-Based Ontologies](https://mlsanity.com/paper/2608.06399/) (2026)

## Abstract (arXiv)

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.