Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6467
Title: CrowdLink: Crowdsourcing for Large-Scale Linked Data Management
Authors: Basharat, Amna
Arpınar, İ. Budak
Dastgheib, Shima
Kurşuncu, Uğur
Kochut, Krys
Doğdu, Erdoğan
Keywords: Crowdsourcing
Semantic Web
Workflow Management
Linking Open Data (LOD)
Human Intelligent Task
Ontology Verification and Entity Disambiguation
Issue Date: 2014
Publisher: IEEE
Source: 8th IEEE International Conference on Semantic Computing -- JUN 16-18, 2014 -- Newport Beach, CA
Series/Report no.: IEEE International Conference on Semantic Computing
Abstract: Crowdsourcing is an emerging paradigm to exploit the notion of human-computation for solving various computational problems, which cannot be accurately solved solely by the machine-based solutions. We use crowdsourcing for large-scale link management in the Semantic Web. More specifically, we develop CrowdLink, which utilizes crowdworkers for verification and creation of triples in Linking Open Data (LOD). LOD incorporates the 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 can help in enriching and improving quality of mission-critical links in LOD. Scalable LOD link management requires a hybrid approach, where human intelligent and machine intelligent tasks interleave in a workflow execution. Likewise, many other crowdsourcing applications require a sophisticated workflow specification not only on human intelligent tasks, but also machine intelligent tasks to handle data and control-flow, which is strictly deficient in the existing crowdsourcing platforms. Hence, we are strongly motivated to investigate the interplay of crowdsourcing, and semantically enriched workflows as well as human-machine cooperation in task completion. We demonstrate usefulness of our approach through various link creation and verification tasks, and workflows using Amazon Mechanical Turk. Experimental evaluation demonstrates promising results in terms of accuracy of the links created, and verified by the crowdworkers
URI: https://doi.org/10.1109/ICSC.2014.14
https://hdl.handle.net/20.500.11851/6467
ISBN: 978-1-4799-4003-5
ISSN: 2325-6516
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

1
checked on Sep 23, 2022

WEB OF SCIENCETM
Citations

1
checked on Sep 24, 2022

Page view(s)

12
checked on Dec 26, 2022

Google ScholarTM

Check

Altmetric


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