Entity resolution in disjoint graphs: An application on genealogical data

Entity Resolution (ER) is the process of identifying references referringto the same entity from one or more data sources. In the ER process, most existing approaches exploit the content information of references, categorized as content-based ER, or additionally consider linkage information among references, categorized as context-based ER. However, in new applications of ER, such as in the genealogicaldomain, the very limited linkage information among referencesresults in a disjoint graph in which the existing content-/context-based ER techniques have very limited applicability. Therefore, in this paper we propose first, to use the homophilyprinciple for augmentation of the original input graph by connecting the potential similar references, and second, to use a Random Walk based approach to consider contextual information available for each referencein the augmented graph. We evaluate the proposed method by applying it to a large genealogicaldataset and we succeed to predict 420,000 referencematches with precision 92% and discover six novel and informative patterns among them which can not be detected in the original disjoint graph.