We present a crowdsourcing system for large-scale production of accurate wrappers to extract data from data-intensive websites. Our approach is based on supervised wrapper inference algorithms which demand the burden of generating training data to workers recruited on a crowdsourcing platform. Workers are paid for answering simple queries carefully chosen by the system. We present two algorithms: a single worker algorithm (ALFη) and a multiple workers algorithm (alfred). Both the algorithms deal with the inherent uncertainty of the workers’ responses and use an active learning approach to select the most informative queries. alfred estimates the workers’ error rate to decide at runtime how many workers should be recruited to achieve a quality target. The system has been fully implemented and tested: the experimental evaluation conducted with both synthetic workers and real workers recruited on a crowdsourcing platform show that our approach is able to produce accurate wrappers at a low cost, even in presence of workers with a significant error rate.

We present a crowdsourcing system for large-scale production of accurate wrappers to extract data from data-intensive websites. Our approach is based on supervised wrapper inference algorithms which demand the burden of generating training data to workers recruited on a crowdsourcing platform. Workers are paid for answering simple queries carefully chosen by the system. We present two algorithms: a single worker algorithm (ALFη) and a multiple workers algorithm (alfred). Both the algorithms deal with the inherent uncertainty of the workers’ responses and use an active learning approach to select the most informative queries. alfred estimates the workers’ error rate to decide at runtime how many workers should be recruited to achieve a quality target. The system has been fully implemented and tested: the experimental evaluation conducted with both synthetic workers and real workers recruited on a crowdsourcing platform show that our approach is able to produce accurate wrappers at a low cost, even in presence of workers with a significant error rate.

Crescenzi, V., Merialdo, P., & Qiu, D. (2015). Crowdsourcing large scale wrapper inference. DISTRIBUTED AND PARALLEL DATABASES, 33(1), 95-122 [10.1007/s10619-014-7163-9].

Crowdsourcing large scale wrapper inference

CRESCENZI, VALTER;MERIALDO, PAOLO;QIU, DISHENG
2015

Abstract

We present a crowdsourcing system for large-scale production of accurate wrappers to extract data from data-intensive websites. Our approach is based on supervised wrapper inference algorithms which demand the burden of generating training data to workers recruited on a crowdsourcing platform. Workers are paid for answering simple queries carefully chosen by the system. We present two algorithms: a single worker algorithm (ALFη) and a multiple workers algorithm (alfred). Both the algorithms deal with the inherent uncertainty of the workers’ responses and use an active learning approach to select the most informative queries. alfred estimates the workers’ error rate to decide at runtime how many workers should be recruited to achieve a quality target. The system has been fully implemented and tested: the experimental evaluation conducted with both synthetic workers and real workers recruited on a crowdsourcing platform show that our approach is able to produce accurate wrappers at a low cost, even in presence of workers with a significant error rate.
We present a crowdsourcing system for large-scale production of accurate wrappers to extract data from data-intensive websites. Our approach is based on supervised wrapper inference algorithms which demand the burden of generating training data to workers recruited on a crowdsourcing platform. Workers are paid for answering simple queries carefully chosen by the system. We present two algorithms: a single worker algorithm (ALFη) and a multiple workers algorithm (alfred). Both the algorithms deal with the inherent uncertainty of the workers’ responses and use an active learning approach to select the most informative queries. alfred estimates the workers’ error rate to decide at runtime how many workers should be recruited to achieve a quality target. The system has been fully implemented and tested: the experimental evaluation conducted with both synthetic workers and real workers recruited on a crowdsourcing platform show that our approach is able to produce accurate wrappers at a low cost, even in presence of workers with a significant error rate.
Crescenzi, V., Merialdo, P., & Qiu, D. (2015). Crowdsourcing large scale wrapper inference. DISTRIBUTED AND PARALLEL DATABASES, 33(1), 95-122 [10.1007/s10619-014-7163-9].
File in questo prodotto:
File Dimensione Formato  
CrowsourcingLargeScaleWrapperInference.pdf

accesso aperto

Tipologia: Versione Editoriale (PDF)
Licenza: DRM non definito
Dimensione 580.76 kB
Formato Adobe PDF
580.76 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11590/307638
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 17
  • ???jsp.display-item.citation.isi??? 13
social impact