HARE: An Engine for Enhancing Answer Completeness of SPARQL Queries via Crowdsourcing

Maribel Acosta, Elena Simperl, Fabian Flöck, Maria Esther Vidal

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

We propose HARE, a SPARQL query engine that encompasses human-machine query processing to augment the completeness of query answers. We empirically assessed the effectiveness of HARE on 50 SPARQL queries over DBpedia. Experimental results clearly show that our solution accurately enhances answer completeness.

Original languageEnglish
Title of host publicationThe Web Conference 2018 - Companion of the World Wide Web Conference, WWW 2018
PublisherAssociation for Computing Machinery, Inc
Pages501-505
Number of pages5
ISBN (Electronic)9781450356404
DOIs
StatePublished - 23 Apr 2018
Externally publishedYes
Event27th International World Wide Web, WWW 2018 - Lyon, France
Duration: 23 Apr 201827 Apr 2018

Publication series

NameThe Web Conference 2018 - Companion of the World Wide Web Conference, WWW 2018

Conference

Conference27th International World Wide Web, WWW 2018
Country/TerritoryFrance
CityLyon
Period23/04/1827/04/18

Keywords

  • RDF
  • SPARQL
  • completeness
  • crowdsourcing
  • query execution

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