Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

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

Version 1 : Received: 6 April 2021 / Approved: 7 April 2021 / Online: 7 April 2021 (11:50:51 CEST)

How to cite: Ali, W.; Saleem, M.; Bin, Y.; Hogan, A.; Ngomo, A.N. A Survey of RDF Stores & SPARQL Engines for Querying Knowledge Graphs. Preprints 2021, 2021040199 (doi: 10.20944/preprints202104.0199.v1). Ali, W.; Saleem, M.; Bin, Y.; Hogan, A.; Ngomo, A.N. A Survey of RDF Stores & SPARQL Engines for Querying Knowledge Graphs. Preprints 2021, 2021040199 (doi: 10.20944/preprints202104.0199.v1).

Abstract

Recent years have seen the growing adoption of non-relational data models for representing diverse, incomplete data. Among these, the RDF graph-based data model has seen ever-broadening adoption, particularly on the Web. This adoption has prompted the standardization of the SPARQL query language for RDF, as well as the development of a variety of local and distributed engines for processing queries over RDF graphs. These engines implement a diverse range of specialized techniques for storage, indexing, and query processing. A number of benchmarks, based on both synthetic and real-world data, have also emerged to allow for contrasting the performance of different query engines, often at large scale. This survey paper draws together these developments, providing a comprehensive review of the techniques, engines and benchmarks for querying RDF knowledge graphs.

Subject Areas

Knowledge Graph;·Storage·Indexing;·Query Processing;·SPARQL;·Benchmarks

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