HARE: A hybrid SPARQL engine to enhance query answers via crowdsourcing

Abstract:

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.

Año de publicación:

2015

Keywords:

• RDF Data
• crowdsourcing
• Crowd Knowledge
• Microtasks
• Hybrid system
• Query execution
• Completeness Model
• SPARQL Query

Fuente:

scopus