QLever: A Query Engine for Efficient SPARQL+Text Search

Hannah Bast, Björn Buchhold

Abstract: We present QLever, a query engine for efficient combined search on a knowledge base and a text corpus, in which named entities from the knowledge base have been identified (that is, recognized and disambiguated). The query language is SPARQL extended by two QLever-specific predicates ql:contains-entity and ql:contains-word, which can express the occurrence of an entity or word (the object of the predicate) in a text record (the subject of the predicate). We evaluate QLever on two large datasets, including FACC (the ClueWeb12 corpus linked to Freebase). We compare against three state-of-the-art query engines for knowledge bases with varying support for text search: RDF-3X, Virtuoso, Broccoli. Query times are competitive and often faster on the pure SPARQL queries, and several orders of magnitude faster on the SPARQL+Text queries. Index size is larger for pure SPARQL queries, but smaller for SPARQL+Text queries.