Datalog Reasoning over Compressed RDF Knowledge Bases

TL;DR

This paper introduces a novel RDF materialisation method that compresses triples and shares structures to reduce memory usage and improve speed, especially with simple rules.

Contribution

It proposes a new compression-based materialisation technique that enables more efficient rule application and fact storage in RDF systems.

Findings

Faster materialisation with less memory for simple rules

Effective compression reduces storage requirements

Outperforms state-of-the-art RDF systems in experiments

Abstract

Materialisation is often used in RDF systems as a preprocessing step to derive all facts implied by given RDF triples and rules. Although widely used, materialisation considers all possible rule applications and can use a lot of memory for storing the derived facts, which can hinder performance. We present a novel materialisation technique that compresses the RDF triples so that the rules can sometimes be applied to multiple facts at once, and the derived facts can be represented using structure sharing. Our technique can thus require less space, as well as skip certain rule applications. Our experiments show that our technique can be very effective: when the rules are relatively simple, our system is both faster and requires less memory than prominent state-of-the-art RDF systems.