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.
@article{arxiv.1803.05714,
title = {r-HUMO: A Risk-Aware Human-Machine Cooperation Framework for Entity Resolution with Quality Guarantees},
author = {Boyi Hou and Qun Chen and Zhaoqiang Chen and Youcef Nafa and Zhanhuai Li},
journal= {arXiv preprint arXiv:1803.05714},
year = {2018}
}
Comments
12 pages, 7 figures. arXiv admin note: text overlap with arXiv:1710.00204