A Human-and-Machine Cooperative Framework for Entity Resolution with Quality Guarantees

For entity resolution, it remains very challenging to find the solution with quality guarantees as measured by both precision and recall. In this demo, we propose a HUman-and-Machine cOoperative framework, denoted by HUMO, for entity resolution. Compared with the existing approaches, HUMO enables a flexible mechanism for quality control that can enforce both precision and recall levels. We also introduce the problem of minimizing human cost given a quality requirement and present corresponding optimization techniques. Finally, we demo that HUMO achieves high-quality results with reasonable return on investment (ROI) in terms of human cost on real datasets.