Utilizing Stacking for Feature Reduction in Graph-Based Genealogical Record Linkage

Genealogy research is centered on collecting records about an individual from various sources and combining the information to gain a larger historical perspective about that individual, commonly in the form of a pedigree. Data extraction, the internet, and other technological advancements have made large amounts of digital genealogical data more accessible. Discovering the relevancy of a digital record to a given pedigree involves determining if the individual described in the record is in actuality an individual within the pedigree. This process is called Genealogical Record Linkage (GRL). GRL can be automated through data mining and techniques by creating machine learned models from hand labeled comparisons. In this paper, we compare two such models-a tabular approach and a graph based stacking approach-and report the successful application of both on a large, post-blocking database. We also note the successful integration of these approaches in an open source distributed genealogy program that finds relevant machetes to a given pedigree from multiple online repositories.