document: ZenCrowd : leveraging probabilistic reasoning and crowdsourcing techniques for large-scale entity linking We tackle the problem of entity linking for large collections of online pages ; Our system , ZenCrowd , identifies entities from natural language text using state of the art techniques and automatically connects them to the Linked Open Data cloud . We show how one can take advantage of human intelligence to improve the quality of the links by dynamically generating micro-tasks on an online crowdsourcing platform . We develop a probabilistic framework to make sensible decisions about candidate links and to identify unreliable human workers . We evaluate ZenCrowd in a real deployment and show how a combination of both probabilistic reasoning and crowdsourcing techniques can significantly improve the quality of the links , while limiting the amount of work performed by the crowd .
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