Repairing of record linkage: Turning errors into insight

Linking records from different data sources, referred to as record linkage, is a longstanding but not yet satisfactorily resolved question in many fields of science. For practitioners, it is difficult to ensure the quality of linkage at the time of applying linkage techniques in real world applications. Instead, linkage errors are often detected later on, mostly by users of the applications. This not only requires us to repair errors, but also provides us with opportunities to observe the linkage quality and uncover why such errors occur. In viewing that record linkage is a complex and evolving process, we study how to acquire insights from linkage errors for achieving high-quality linkage. We propose a generic repairing framework which allows us to start with imperfect linkage models, and dynamically repair linkage models and errors for improved linkage quality. We have evaluated our repairing framework over three real-world datasets and the experimental results show that the performance of the proposed tree-structured classifier SVM-tree outperforms the baseline methods.