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

TL;DR

This paper introduces a column-oriented approach to Datalog materialization over large knowledge graphs, combining memory layout and optimizations to improve efficiency and reduce redundancy, often outperforming existing systems.

Contribution

It presents a novel column-based memory layout with optimization techniques and proactive caching for efficient Datalog inference on large KGs.

Findings

Often matches or surpasses state-of-the-art performance

Effective under resource constraints

Reduces redundant inferences at runtime

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.