Home |  DEUTSCH |  Contact |  Imprint |  Login |  KIT

Article3143

Aus Aifbportal

Wechseln zu: Navigation, Suche

(This page contains COinS metadata)

Detecting Linked Data Quality Issues via Crowdsourcing: A DBpedia Study


Maribel Acosta, Amrapali Zaveri, Elena Simperl, Dimitris Kontokostas, Fabian Flöck, Jens Lehmann



Veröffentlicht: 2017

Journal: Semantic Web Journal


Verlag: IOS Press
Volume: Special issue on Human Computation and Crowdsourcing (HC&C) in the Context of the Semantic Web

Nicht-referierte Veröffentlichung
BibTeX




Kurzfassung
In this paper we examine the use of crowdsourcing as a means to detect Linked Data quality problems that are difficult to uncover automatically. We base our approach on the analysis of the most common errors encountered in the DBpedia dataset, and a classification of these errors according to the extent to which they are likely to be amenable to crowdsourcing. We then propose and study different crowdsourcing approaches to identify these Linked Data quality issues, employing DBpedia as our use case: (i) a contest targeting the Linked Data expert community, and (ii) paid microtasks published on Amazon Mechanical Turk. We secondly focus on adapting the Find-Fix-Verify crowdsourcing pattern to exploit the strengths of experts and lay workers. By testing two distinct Find-Verify workflows (lay users only and experts verified by lay users) we reveal how to best combine different crowds’ complementary aptitudes in Linked Data quality issue detection. The results show that a combination of the two styles of crowdsourcing is likely to achieve more efficient results than each of them used in isolation, and that human computation is a promising and affordable way to enhance the quality of DBpedia.

Weitere Informationen unter: Link



Forschungsgruppe

Web Science