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.
[2] A 2023 study compared QLever with Virtuoso, Blazegraph, GraphDB, Stardog, Apache Jena, and Oxigraph.
The QLever version investigated in the study achieved fast execution of successful queries but offered limited support for complex SPARQL constructs.
[3] The official QLever instance provides API endpoints for querying the following datasets:[4] 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.