@inproceedings{6391b46e7f2b4086ab7d7d16a02a9c7c,
title = "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.",
keywords = "Completeness Model, Crowd Knowledge, Crowdsourcing, Hybrid System, Microtasks, Query Execution, RDF Data, SPARQL Query",
author = "Maribel Acosta and Elena Simperl and Fabian Fl{\"o}ck and Vidal, {Maria Esther}",
note = "Publisher Copyright: {\textcopyright} 2015 ACM.; 8th International Conference on Knowledge Capture, K-CAP 2015 ; Conference date: 07-10-2015 Through 10-10-2015",
year = "2015",
month = oct,
day = "7",
doi = "10.1145/2815833.2815848",
language = "English",
series = "Proceedings of the 8th International Conference on Knowledge Capture, K-CAP 2015",
publisher = "Association for Computing Machinery, Inc",
booktitle = "Proceedings of the 8th International Conference on Knowledge Capture, K-CAP 2015",
}