Waldo

Abstract

In Entity Resolution, the objective is to find which records of a dataset refer to the same real-world entity. Crowd Entity Resolution uses humans, in addition to machine algorithms, to improve the quality of the outcome. We study a hybrid approach that combines two common interfaces for human tasks in Crowd Entity Resolution, taking into account key observations about the advantages and disadvantages of the two interfaces. We give a formal definition to the problem of human task selection and we derive algorithms with strong optimality guarantees. Our experiments with four real-world datasets show that our hybrid approach gives an improvement of 50% to 300% in the crowd cost to resolve a dataset, compared to using a single interface.

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