  Even though many machine algorithms have been proposed for entity resolution,
it remains very challenging to find a solution with quality guarantees. In this
paper, we propose a novel HUman and Machine cOoperation (HUMO) framework for
entity resolution (ER), which divides an ER workload between the machine and
the human. HUMO enables a mechanism for quality control that can flexibly
enforce both precision and recall levels. We introduce the optimization problem
of HUMO, minimizing human cost given a quality requirement, and then present
three optimization approaches: a conservative baseline one purely based on the
monotonicity assumption of precision, a more aggressive one based on sampling
and a hybrid one that can take advantage of the strengths of both previous
approaches. Finally, we demonstrate by extensive experiments on real and
synthetic datasets that HUMO can achieve high-quality results with reasonable
return on investment (ROI) in terms of human cost, and it performs considerably
better than the state-of-the-art alternatives in quality control.
