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      "@value" : "In this paper we examine the use of crowdsourcing as a means to master Linked Data quality problems that are difficult to solve automatically. We base our approach on the analysis of the most common errors encountered in Linked Data sources, and a classification of these errors according to the extent to which they are likely to be amenable to crowdsourcing. We then propose and compare different crowdsourcing approaches to identify these Linked Data quality issues, employing the DBpedia dataset 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 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 Linked Data."
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