Linking historical census data across time

Historical census data provide a snapshot of the era when our ancestors lived. Such data contain valuable information for the reconstruction of households and the tracking of family changes across time, which can be used for a variety of social science research projects. As valuable as they are, these data provide only snapshots of the main characteristics of the stock of a population. To capture household changes requires that we link person by person and household by household from one census to the next over a series of censuses. Once linked together, the census data are greatly enhanced in value. Development of an automatic or semi-automatic linking procedure will significantly relieve social scientists from the tedious task of manually linking individuals, families, and households, and can lead to an improvement of their productivity. In this thesis, a systematic solution is proposed for linking historical census data that integrates data cleaning and standardisation, as well as record and household linkage over consecutive censuses. This solution consists of several data pre-processing, machine learning, and data mining methods that address different aspects of the historical census data linkage problem. A common property of these methods is that they all adopt a strategy to consider a household as an entity, and use the whole of household information to improve the effectiveness of data cleaning and the accuracy of record and household linkage. We first proposal an approach for automatic cleaning and linking using domain knowledge. The core idea is to use household information in both the cleaning and linking steps, so that records that contain errors and variations can be cleaned and standardised and the number of wrongly linked records can be reduced. Second, we introduce a group linking method into household linkage, which enables tracking of the majority of members in a household over a period of time. The proposed method is based on the outcome of the record linkage step using either a similarity based method or a machine learning approach. A group linking method is then applied, aiming to reduce ambiguity of multiple household linkages. Third, we introduce a graph-based method to link households, which takes the structural relationship between household members into consideration. Based on the results of linking individual records, our method builds a graph for each household, so that the matches of household's in different census are determined by both attribute relationship and record similarities. This allows household similarities be more accurately calculated. Finally, we describe an instance classification method based on a multiple instance learning method. This allows an integrated solution to link both households and individual records at the same time. Our method treats group links as bags and individual record links as instances. We extend multiple instance learning from bag to instance classification in order to allow the reconstruction of bags from candidate instances. The classified bag and instance samples lead to a significant reduction in multiple group links, thereby improving the overall quality of linked data

A precise blocking method for record linkage

Identifying approximately duplicate records between databases requires the costly computation of distances between their attributes. Thus duplicate detection is usually performed in two phases, an efficient blocking phase that determines few potential candidate duplicates based on simple criteria, followed by a second phase performing an in-depth comparison of the candidate duplicates. This paper introduces and evaluates a precise and efficient approach for the blocking phase, which requires only standard indices, but performs as well as other approaches based on special purpose indices, and outperforms other approaches based on standard indices. The key idea of the approach is to use a comparison window with a size that depends dynamically on a maximum distance, rather than using a window with fixed size