  Even though many approaches have been proposed for entity resolution (ER), it
remains very challenging to find one with quality guarantees. To this end, we
proposea risk-aware HUman-Machine cOoperation framework for ER, denoted by
r-HUMO. Built on the existing HUMO framework, r-HUMO similarly enforces both
precision and recall levels by partitioning an ER workload between the human
and the machine. However, r-HUMO is the first solution to optimize the process
of human workload selection from a risk perspective. It iteratively selects
human workload based on real-time risk analysis on human-labeled results as
well as prespecified machine metrics. In this paper,we first introduce the
r-HUMO framework and then present the risk analysis technique to prioritize the
instances for manual labeling. Finally,we empirically evaluate r-HUMO's
performance on real data. Our extensive experiments show that r-HUMO is
effective in enforcing quality guarantees,and compared with the
state-of-the-art alternatives, it can achieve better quality control with
reduced human cost.
