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

TL;DR

RDF-Hunter is a hybrid system that combines machine and human computation to improve the completeness of query answers over RDF datasets by selectively crowdsourcing parts of query execution.

Contribution

It introduces a novel quality model and query engine that dynamically decides when to involve crowdsourcing to enhance RDF query results.

Findings

Significantly improves answer completeness for RDF queries.

Feasible hybrid approach demonstrated on DBpedia dataset.

Reliable enhancement of automatic query responses.

Abstract

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