QLever
| Autor: | Björn Buchhold, Hannah Bast |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Text corpus
Information retrieval Computer science business.industry InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL Search engine indexing InformationSystems_DATABASEMANAGEMENT Full text search 02 engineering and technology computer.file_format Query language Named graph Knowledge base 020204 information systems ComputingMethodologies_DOCUMENTANDTEXTPROCESSING 0202 electrical engineering electronic engineering information engineering SPARQL 020201 artificial intelligence & image processing business computer |
| Zdroj: | CIKM |
| DOI: | 10.1145/3132847.3132921 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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