Multifile Partitioning for Record Linkage and Duplicate Detection
This work addresses a practical gap in record linkage and duplicate detection for scenarios involving multiple files, which is common in real-world data integration tasks.
The paper tackles the problem of merging multiple datafiles with overlapping entities and duplicates, proposing a Bayesian approach that uses a novel partition representation and flexible prior information. The method's performance is evaluated through extensive simulations, with code made available for implementation.
Merging datafiles containing information on overlapping sets of entities is a challenging task in the absence of unique identifiers, and is further complicated when some entities are duplicated in the datafiles. Most approaches to this problem have focused on linking two files assumed to be free of duplicates, or on detecting which records in a single file are duplicates. However, it is common in practice to encounter scenarios that fit somewhere in between or beyond these two settings. We propose a Bayesian approach for the general setting of multifile record linkage and duplicate detection. We use a novel partition representation to propose a structured prior for partitions that can incorporate prior information about the data collection processes of the datafiles in a flexible manner, and extend previous models for comparison data to accommodate the multifile setting. We also introduce a family of loss functions to derive Bayes estimates of partitions that allow uncertain portions of the partitions to be left unresolved. The performance of our proposed methodology is explored through extensive simulations. Code implementing the methodology is available at https://github.com/aleshing/multilink .