Inproceedings3610
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HARE: An Engine for Enhancing Answer Completeness of SPARQL Queries via Crowdsourcing
HARE: An Engine for Enhancing Answer Completeness of SPARQL Queries via Crowdsourcing
Published: 2018
April
Buchtitel: WWW ’18 Companion: The 2018 Web Conference Companion
Verlag: ACM
Erscheinungsort: New York, NY, USA
Organisation: The 2018 Web Conference
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Kurzfassung
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.
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DOI Link: 10.1145/3184558.3186241
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