# Optimised Storage for Datalog Reasoning

arXiv: https://arxiv.org/abs/2312.11297 · HTML: https://mlsanity.com/paper/2312.11297/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 22 references that are in the map.
- First posted on arXiv 18 Dec 2023.

## Builds on (most cited first, 2 of 2)

- [The Vadalog System: Datalog-based Reasoning for Knowledge Graphs](https://mlsanity.com/paper/1807.08709/) (2018)
- [Handling owl:sameAs via Rewriting](https://mlsanity.com/paper/1411.3622/) (2014)

## Abstract (arXiv)

Materialisation facilitates Datalog reasoning by precomputing all consequences of the facts and the rules so that queries can be directly answered over the materialised facts. However, storing all materialised facts may be infeasible in practice, especially when the rules are complex and the given set of facts is large. We observe that for certain combinations of rules, there exist data structures that compactly represent the reasoning result and can be efficiently queried when necessary. In this paper, we present a general framework that allows for the integration of such optimised storage schemes with standard materialisation algorithms. Moreover, we devise optimised storage schemes targeting at transitive rules and union rules, two types of (combination of) rules that commonly occur in practice. Our experimental evaluation shows that our approach significantly improves memory consumption, sometimes by orders of magnitude, while remaining competitive in terms of query answering time.