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{{Inproceedings
 
{{Inproceedings
|Referiert=False
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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  
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|Organization=International Conference on Knowledge Capture
 
|Publisher=ACM
 
|Publisher=ACM
 
}}
 
}}
 
{{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.
|Forschungsgruppe=Wissensmanagement
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|Forschungsgruppe=Web Science
 
}}
 
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Aktuelle Version vom 30. April 2018, 16:47 Uhr


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.



Forschungsgruppe

Web Science


Forschungsgebiet