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In the last years, a large number of RDF data sets has become available onthe Web. However, due to the semi-structured nature of RDF data, missing valuesaffect answer completeness of queries that are posed against this data. Toovercome this limitation, we propose RDF-Hunter, a novel hybrid queryprocessing approach that brings together machine and human computation toexecute queries against RDF data. We develop a novel quality model and queryengine in order to enable RDF-Hunter to on the fly decide which parts of aquery should be executed through conventional technology or crowd computing. Toevaluate RDF-Hunter, we created a collection of 50 SPARQL queries against theDBpedia data set, executed them using our hybrid query engine, and analyzed theaccuracy of the outcomes obtained from the crowd. The experiments clearly showthat the overall approach is feasible and produces query results that reliablyand significantly enhance completeness of automatic query processing responses.

RDF-Hunter: Automatically Crowdsourcing the Execution of Queries Against RDF Data Sets