• Due to the semi-structured nature of RDF data, missing values affect answer completeness of queries that are posed against RDF. To overcome this limitation, we present HARE, a novel hybrid query processing engine that brings together machine and human computation to execute SPARQL queries. We propose a model that exploits the characteristics of RDF in order to estimate the completeness of portions of a data set. The completeness model complemented by crowd knowledge is used by the HARE query engine to on-the-fly decide which parts of a query should be executed against the data set or via crowd computing. To evaluate HARE, we created and executed a collection of 50 SPARQL queries against the DBpedia data set. Experimental results clearly show that our solution accurately enhances answer completeness. ()
  • K-CAP ()
  • 7 ()
is ?:cites of
  • 2016-06-24 ()
  • 10.1145/2815833.2815848 ()
  • 7 ()
is ?:hasCitedEntity of
  • en ()
  • 2015-10-07 ()
  • ACM ()
  • 20641 ()
  • 15 ()
  • 11 ()
  • HARE: A Hybrid SPARQL Engine to Enhance Query Answers via Crowdsourcing ()


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