QLever
QLever (pronounced /ˈklɛvər/ KLEH-ver, as in "clever") is an open-source triplestore and graph database developed by a team at the University of Freiburg led by Hannah Bast. QLever performs high-performance queries of semantic Web knowledge bases, including full-text search within text corpuses.[1] A specialized user interface for QLever predictively autocompletes SPARQL queries.[2] CharacteristicsA 2023 study compared QLever with Virtuoso, Blazegraph, GraphDB, Stardog, Apache Jena, and Oxigraph. The study investigated a QLever version from 2021, concluding that it achieved fast execution of successful queries but offered limited support for complex SPARQL constructs.[3][4] ContentsThe official QLever instance provides API endpoints for querying the following datasets:[5]
For OpenStreetMap and OpenHistoricalMap data, the QLever engine supports a limited subset of GeoSPARQL functions, supplemented by a precomputed subset of GeoSPARQL relationships stored as dedicated triples.[6] AdoptionBesides the official instance, the QLever engine also powers the official SPARQL endpoint of DBLP.[7] QLever is one of the candidates to replace Blazegraph as the triplestore for the Wikidata Query Service.[3][8] See also Wikimedia Commons has media related to QLever. References
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