关键词: 实体解析, 众包, MapReduce编程模型, 隐私保护, 患者主索引

Abstract: Entity resolution is to find and cluster records that refer to the same real-world object. It can be an extremely difficult process to get high accuracy for computer algorithms alone although they can bring high efficiency. This paper proposes a hybrid approach combining machine processing with crowdsourcing for entity resolution. Firstly the record pairs that are impossible to match are excluded by MapReduce-based parallel computing framework so as to reduce the number of human intelligence tasks, and then those ambiguous record pairs are labeled by human operation. A role-based access control model and related information hiding strategies are adopted for privacy protection during the crowdsourcing sessions. The approach and the model are applied on the master patient index building platform for a hospital. The experimental results show that they make full use of the advantages of machine-based and human-based processing ways, bring high efficiency and accuracy for patient entity resolution, and avoid the leakage of patient information.

Key words: entity resolution, crowdsourcing, MapReduce programming model, privacy protection, master patient index