Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/3311
Title: Semantically Enriched Task and Workflow Automation in Crowdsourcing for Linked Data Management
Authors: Basharat, Amna
Arpinar, I. Budak
Dastgheib, Shima
Kursuncu, Ugur
Kochut, Krys
Doğdu, Erdogan
Keywords: Crowdsourcing
human intelligent task
Linked Open Data (LOD)
ontology verification and entity disambiguation
semantic web
workflow management
Issue Date: Dec-2014
Publisher: World Scientific Publishing Co. Pte Ltd
Source: Basharat, A., Arpinar, I. B., Dastgheib, S., Kursuncu, U., Kochut, K., and Dogdu, E. (2014). Semantically enriched task and workflow automation in crowdsourcing for linked data management. International Journal of Semantic Computing, 8(04), 415-439.
Abstract: Crowdsourcing is one of the new emerging paradigms to exploit the notion of human-computation for harvesting and processing complex heterogenous data to produce insight and actionable knowledge. Crowdsourcing is task-oriented, and hence specification and management of not only tasks, but also workflows should play a critical role. Crowdsourcing research can still be considered in its infancy. Significant need is felt for crowdsourcing applications to be equipped with well defined task and workflow specifications ranging from simple human-intelligent tasks to more sophisticated and cooperative tasks to handle data and control-flow among these tasks. Addressing this need, we have attempted to devise a generic, flexible and extensible task specification and workflow management mechanism in crowdsourcing. We have contextualized this problem to linked data management as our domain of interest. More specifically, we develop CrowdLink, which utilizes an architecture for automated task specification, generation, publishing and reviewing to engage crowdworkers for verification and creation of triples in the Linked Open Data (LOD) cloud. The LOD incorporates various core data sets in the semantic web, yet is not in full conformance with the guidelines for publishing high quality linked data on the web. Our approach is not only useful in efficiently processing the LOD management tasks, it can also help in enriching and improving quality of mission-critical links in the LOD. We demonstrate usefulness of our approach through various link creation and verification tasks, and workflows using Amazon Mechanical Turk. Experimental evaluation demonstrates promising results not only in terms of ease of task generation, publishing and reviewing, but also in terms of accuracy of the links created, and verified by the crowdworkers. © 2014 World Scientific Publishing Company.
URI: https://www.worldscientific.com/doi/abs/10.1142/S1793351X14400133
https://hdl.handle.net/20.500.11851/3311
ISSN: 1793351X
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Show full item record

CORE Recommender

SCOPUSTM   
Citations

5
checked on Sep 23, 2022

WEB OF SCIENCETM
Citations

6
checked on Sep 24, 2022

Page view(s)

24
checked on Dec 26, 2022

Google ScholarTM

Check

Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.