A Survey of RDF Stores & SPARQL Engines for Querying Knowledge Graphs

TL;DR

This paper provides a comprehensive survey of current RDF storage, indexing, and query processing techniques for SPARQL engines, focusing on local systems and including a review of over one hundred engines and benchmarks.

Contribution

It offers the first extensive survey of state-of-the-art local RDF stores and SPARQL query engines, including techniques, benchmarks, and research challenges.

Findings

Reviewed over 100 SPARQL query engines.

Analyzed 12 benchmarks and their features.

Identified key research challenges for improving query engines.

Abstract

RDF has seen increased adoption in recent years, prompting the standardization of the SPARQL query language for RDF, and the development of local and distributed engines for processing SPARQL queries. This survey paper provides a comprehensive review of techniques and systems for querying RDF knowledge graphs. While other reviews on this topic tend to focus on the distributed setting, the main focus of the work is on providing a comprehensive survey of state-of-the-art storage, indexing and query processing techniques for efficiently evaluating SPARQL queries in a local setting (on one machine). To keep the survey self-contained, we also provide a short discussion on graph partitioning techniques used in the distributed setting. We conclude by discussing contemporary research challenges for further improving SPARQL query engines. This extended version also provides a survey of over one hundred SPARQL query engines and the techniques they use, along with twelve benchmarks and their features.