Crowdsourcing tasks within linked data management
Crowdsourcing tasks within linked data management
Many aspects of Linked Data management – including exposing legacy data and applications to semantic formats, designing vocabularies to describe RDF data, identifying links between entities, query processing, and data curation– are necessarily tackled through the combination of human effort with algorithmic techniques. In the literature on traditional data management the theoretical and technical groundwork to realize and manage such combinations is being established. In this paper we build upon and extend these ideas to propose a framework by which human and computational intelligence can co-exist by augmenting existing Linked Data and Linked Service technology with crowdsourcing functionality. Starting from a motivational scenario we introduce a set of generic tasks which may feasibly be approached using crowdsourcing platforms such as Amazon’s Mechanical Turk, explain how these tasks can be decomposed and translated into MTurk projects, and roadmap the extensions to SPARQL, D2RQ/R2R and Linked Data browsing that are required to achieve this vision.
Simperl, E.
40261ae4-c58c-48e4-b78b-5187b10e4f67
Norton, B.
24e57777-0135-412a-b93f-d24fca273091
Vrandecic, D.
2642fe14-9606-4ee3-8616-67f30849f3b3
23 October 2011
Simperl, E.
40261ae4-c58c-48e4-b78b-5187b10e4f67
Norton, B.
24e57777-0135-412a-b93f-d24fca273091
Vrandecic, D.
2642fe14-9606-4ee3-8616-67f30849f3b3
Simperl, E., Norton, B. and Vrandecic, D.
(2011)
Crowdsourcing tasks within linked data management.
Second International Workshop on Consuming Linked Data (COLD 2011), Bonn, Germany.
22 Oct 2011.
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Conference or Workshop Item
(Paper)
Abstract
Many aspects of Linked Data management – including exposing legacy data and applications to semantic formats, designing vocabularies to describe RDF data, identifying links between entities, query processing, and data curation– are necessarily tackled through the combination of human effort with algorithmic techniques. In the literature on traditional data management the theoretical and technical groundwork to realize and manage such combinations is being established. In this paper we build upon and extend these ideas to propose a framework by which human and computational intelligence can co-exist by augmenting existing Linked Data and Linked Service technology with crowdsourcing functionality. Starting from a motivational scenario we introduce a set of generic tasks which may feasibly be approached using crowdsourcing platforms such as Amazon’s Mechanical Turk, explain how these tasks can be decomposed and translated into MTurk projects, and roadmap the extensions to SPARQL, D2RQ/R2R and Linked Data browsing that are required to achieve this vision.
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Published date: 23 October 2011
Venue - Dates:
Second International Workshop on Consuming Linked Data (COLD 2011), Bonn, Germany, 2011-10-22 - 2011-10-22
Organisations:
Web & Internet Science
Identifiers
Local EPrints ID: 351665
URI: http://eprints.soton.ac.uk/id/eprint/351665
PURE UUID: 35451590-c33a-41a6-bf80-8179bb454661
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Date deposited: 29 Apr 2013 15:49
Last modified: 11 Dec 2021 04:41
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Contributors
Author:
B. Norton
Author:
D. Vrandecic
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