A survey of RDF stores & SPARQL engines for querying knowledge graphs

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. An 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.
SKA South Africa 860801
Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT)
CONICYT FONDECYT 1181896
ANID-Millennium Science Initiative Program ICN17_002
National Natural Science Foundation of China (NSFC) 61922054
61872235
61832017
61729202
61832013
National Key Research and Development Program of China 2020YFB1710202
2018YFC1504504
Science and Technology Commission of Shanghai Municipality (STCSM) AI 19511120300
Federal Ministry of Education & Research (BMBF) E1114681
01QE2114
project KnowGraphs 860801
Versión sometida a revisión - Preprint