Detecting Linked Data quality issues via crowdsourcing: A DBpedia study-Reference-Cited by-同舟云学术

Detecting Linked Data quality issues via crowdsourcing: A DBpedia study

Published:2018-04-12 Issue:3 Volume:9 Page:303-335
ISSN:2210-4968
Container-title:Semantic Web
language:
Short-container-title:SW

Author:

Acosta Maribel¹,Zaveri Amrapali²,Simperl Elena³,Kontokostas Dimitris²,Flöck Fabian⁴,Lehmann Jens²

Affiliation:

1. Institute AIFB, Karlsruhe Institute of Technology, Germany. E-mail: maribel.acosta@kit.edu

2. Institut für Informatik, AKSW, Universität Leipzig, Germany. E-mails: zaveri@informatik.uni-leipzig.de, kontokostas@informatik.uni-leipzig.de, lehmann@informatik.uni-leipzig.de

3. Web Science and Internet Research Group, University of Southampton, United Kingdom. E-mail: e.simperl@soton.ac.uk

4. Computational Social Science Group, GESIS – Leibniz Institute for the Social Sciences, Germany. E-mail: fabian.floeck@gesis.org

Publisher

IOS Press

Subject

Computer Networks and Communications,Computer Science Applications,Information Systems

Reference56 articles.

1. HARE

2. Crowdsourcing Linked Data Quality Assessment

3. Crowdsourcing and the Semantic Web;Bernstein;Dagstuhl Reports,2014

4. Soylent

5. Quality-driven information filtering using the WIQA policy framework;Bizer;Journal of Web Semantics,2009

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