Select Your Questions Wisely: For Entity Resolution With Crowd Errors

Crowdsourcing is becoming increasingly important in entity resolution tasks due to their inherent complexity such as clustering of images and natural language processing. Humans can provide more insightful information for these difficult problems compared to machine-based automatic techniques. Nevertheless, human workers can make mistakes due to lack of domain expertise or seriousness, ambiguity, or even due to malicious intents. The bulk of literature usually deals with human errors via majority voting or by assigning a universal error rate over crowd workers. However, such approaches are incomplete, and often inconsistent, because the expertise of crowd workers are diverse with possible biases, thereby making it largely inappropriate to assume a universal error rate for all workers over all crowdsourcing tasks. We mitigate the above challenges by considering an uncertain graph model, where the edge probability between two records A and B denotes the ratio of crowd workers who voted YES on the question if A and B are same entity. To reflect independence across different crowdsourcing tasks, we apply the notion of possible worlds, and develop parameter-free algorithms for both next crowdsourcing and entity resolution tasks. In particular, for next crowdsourcing, we identify the record pair that maximally increases the reliability of the current clustering. Since reliability takes into account the connected-ness inside and across all clusters, this metric is more effective in deciding next questions, in comparison with state-of-the-art works, which consider local features, such as individual edges, paths, or nodes to select next crowdsourcing questions. Based on detailed empirical analysis over real-world datasets, we find that our proposed solution, PERC (probabilistic entity resolution with imperfect crowd) improves the quality by 15% and reduces the overall cost by 50% for the crowdsourcing-based entity resolution.