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|Title=HARE: A Hybrid SPARQL Engine to Enhance Query Answers via Crowdsourcing | |Title=HARE: A Hybrid SPARQL Engine to Enhance Query Answers via Crowdsourcing | ||
|Year=2015 | |Year=2015 | ||
|Month=Oktober | |Month=Oktober | ||
|Booktitle=K-CAP2015, The 8th International Conference on Knowledge Capture | |Booktitle=K-CAP2015, The 8th International Conference on Knowledge Capture | ||
| − | |Organization=International Conference on Knowledge Capture | + | |Organization=International Conference on Knowledge Capture |
|Publisher=ACM | |Publisher=ACM | ||
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{{Publikation Details | {{Publikation Details | ||
|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 complete- ness 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. | |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 complete- ness 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. | ||
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Aktuelle Version vom 30. April 2018, 16:47 Uhr
HARE: A Hybrid SPARQL Engine to Enhance Query Answers via Crowdsourcing
HARE: A Hybrid SPARQL Engine to Enhance Query Answers via Crowdsourcing
Published: 2015
Oktober
Buchtitel: K-CAP2015, The 8th International Conference on Knowledge Capture
Verlag: ACM
Organisation: International Conference on Knowledge Capture
Referierte Veröffentlichung
BibTeX
Kurzfassung
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 complete- ness 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.