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One of the goals of our work is to investigate how the contributions of crowdsourcing approaches can be integrated into LD curation processes, by evaluating the performance of two crowdsourcing workflows in a cost-efficient way. In microtask settings, the first challenge is then to reduce the amount of tasks submitted to the crowd and the number of requested assign- ments (different answers), since both of these factors determine the overall cost of crowdsourcing projects. For the Find stage, Algorithm 1 generated 2, 339 HITs to crowdsource 68, 976 RDF triples, consistently with the property stated by Proposition 2. In our experiments, we approved a total of 2, 294 task solutions in the Find stage and, considering the payment per HIT (US$ 0.06), the total cost of this evaluation resulted in US$ 137.58. Furthermore, in the Verify stage, the cost of submitting to MTurk the problematic triples found by the experts was only US$ 43.
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