Multiple instance learning for group record linkage
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
A supervised learning and group linking method for historical census household linkage
AusDM '11 Proceedings of the Ninth Australasian Data Mining Conference - Volume 121
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Historical census data captures information about our ancestors. These data contain the social status at a certain point time. They contain valuable information for genealogists, historians, and social scientists. Historical census data can be used to reconstruct important aspects of a particular era in order to trace the changes in households and families. Record linkage across different historical census datasets can help to improve the quality of the data, enrich existing census data with additional information, and facilitate improved retrieval of information. In this paper, we introduce a domain driven approach to automatically clean and link historical census data based on recent developments in group linkage techniques. The key contribution of our approach is to first detect households, and to use this information to refine the cleaned data and improve the accuracy of linking records between census datasets. We have developed a two-step linking approach, which first links individual records using approximate string similarity measures, and then performs a group linking based on the previously detected households. The results show that this approach is effective and can greatly reduce the manual efforts required for data cleaning and linking by social scientists.