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An experiment in comparing human-computation techniques

An experiment in comparing human-computation techniques
An experiment in comparing human-computation techniques
Human computation can address complex computational problems by tapping into large resource pools for relatively little cost. Two prominent human-computation techniques - games with a purpose (GWAP) and microtask crowdsourcing - can help resolve semantic-technology-related tasks, including knowledge representation, ontology alignment, and semantic annotation. To evaluate which approach is better with respect to costs and benefits, the authors employ categorization challenges in Wikipedia to ultimately create a large, general-purpose ontology. They first use the OntoPronto GWAP, then replicate its problem-solving setting in Amazon Mechanical Turk, using a similar task-design structure, evaluation mechanisms, and input data.
gwap, mechanical turk, conceptual modeling, crowdsourcing, incentives, motivators, ontology
52-58
Thaler, S.
1447e191-5c07-4e51-b406-dbb41bedcfda
Simperl, E.
40261ae4-c58c-48e4-b78b-5187b10e4f67
Wölger, S.
1283a019-c0f6-42c5-a73d-080235ae1269
Thaler, S.
1447e191-5c07-4e51-b406-dbb41bedcfda
Simperl, E.
40261ae4-c58c-48e4-b78b-5187b10e4f67
Wölger, S.
1283a019-c0f6-42c5-a73d-080235ae1269

Thaler, S., Simperl, E. and Wölger, S. (2012) An experiment in comparing human-computation techniques IEEE Internet Computing, 16, (5), pp. 52-58.

Record type: Article

Abstract

Human computation can address complex computational problems by tapping into large resource pools for relatively little cost. Two prominent human-computation techniques - games with a purpose (GWAP) and microtask crowdsourcing - can help resolve semantic-technology-related tasks, including knowledge representation, ontology alignment, and semantic annotation. To evaluate which approach is better with respect to costs and benefits, the authors employ categorization challenges in Wikipedia to ultimately create a large, general-purpose ontology. They first use the OntoPronto GWAP, then replicate its problem-solving setting in Amazon Mechanical Turk, using a similar task-design structure, evaluation mechanisms, and input data.

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More information

e-pub ahead of print date: 15 May 2012
Published date: September 2012
Keywords: gwap, mechanical turk, conceptual modeling, crowdsourcing, incentives, motivators, ontology
Organisations: Web & Internet Science

Identifiers

Local EPrints ID: 351592
URI: http://eprints.soton.ac.uk/id/eprint/351592
PURE UUID: 49614ab4-97c9-4880-bfd2-a0dbab3a09db
ORCID for E. Simperl: ORCID iD orcid.org/0000-0003-1722-947X

Catalogue record

Date deposited: 23 Apr 2013 13:01
Last modified: 10 Nov 2017 19:05

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Contributors

Author: S. Thaler
Author: E. Simperl ORCID iD
Author: S. Wölger

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