期刊名称：Bulletin of the Technical Committee on Data Engineering
出版年度：2018
卷号：41
期号：2
页码：104-115
出版社：IEEE Computer Society
摘要：Rule-based data integration approaches are widely adopted due to its better interpretability and effectiveinteractive debugging. However, it is very challenging to generate high-quality rules for data integrationtasks. Hand-crafted rules from domain experts are usually reliable, but they are not scalable: it is timeand effort consuming to handcraft many rules with large coverage over the data. On the other hand,weak-supervision rules automatically generated from machines, such as distant supervision rules, canlargely cover the items; however, they may be very noisy that provide many wrong results. To addressthe problem, we propose a human-in-the-loop rule learning approach with high coverage and highquality. The approach first generates a set of candidate rules, and proposes a machine-based methodto learn a confidence for each rule using generative adversarial networks. Then, it devises a game-based crowdsourcing framework to refine the rules, and develops a budget-constraint crowdsourcingalgorithm for rule refinement at affordable cost. Finally, it applies the rules to produce high-quality dataintegration results..
