Find truth in the hands of the few: acquiring specific knowledge with crowdsourcing

Crowdsourcing has been a helpful mechanism to leverage human intelligence to acquire useful knowledge. However, when we aggregate the crowd knowledge based on the currently developed voting algorithms, it often results in common knowledge that may not be expected. In this paper, we consider the problem of collecting specific knowledge via crowdsourcing. With the help of using external knowledge base such as WordNet, we incorporate the semantic relations between the alternative answers into a probabilistic model to determine which answer is more specific. We formulate the probabilistic model considering both worker’s ability and task’s difficulty from the basic assumption, and solve it by the expectation-maximization (EM) algorithm. To increase algorithm compatibility, we also refine our method into semi-supervised one. Experimental results show that our approach is robust with hyper-parameters and achieves better improvement than majority voting and other algorithms when more specific answers are expected, especially for sparse data.