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Table 4 exhibits that the experts are reliable (with 0.83 of precision) on finding this type of quality issue, while the precision of the crowd (0.51) on verifying these triples is relatively low. In particular, the first answers submitted by the crowd were slightly better than the results obtained with majority voting. A detailed study of these cases showed that 28 triples that were initially classified correctly, later were misclassified, and most of these triples refer to a language datatype. The low performance of the MTurk workers compared to the experts is not surprising, since this particular task requires certain technical knowledge about datatypes and, moreover, the specification of values and types in LD.
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