Robust Temporal Graph Clustering for Group Record Linkage
Research in the social sciences is increasingly based on large and complex data collections,where individual data sets from different domains need to be linked to allow advanced analytics.A popular type of data used in such a context are historical registries containing birth,death,and marriage certificates.Individually,such data sets however limit the types of studies that can be conducted.Specifically,it is impossible to track individuals,families,or households over time.Once such data sets are linked and family trees are available it is possible to,for example,investigate how education,health,mobility,and employment influence the lives of people over two or even more generations.The linkage of historical records is challenging because of data quality issues and because often there are no ground truth data available.Unsupervised techniques need to be employed,which generally are based on similarity graphs generated by comparing individual records.In this paper we present a novel temporal clustering approach aimed at linking records of the same group(such as all births by the same mother)where temporal constraints(such as intervals between births)need to be enforced.We combine a connected component approach with an iterative merging step which considers temporal constraints to obtain accurate clustering results.Experiments on a real Scottish data set show the superiority of our approach over a previous clustering approach for record linkage.
Entity resolution Star clustering Vital records Birth bundling
Charini Nanayakkara Peter Christen Thilina Ranbaduge
Research School of Computer Science,The Australian National University,Canberra,ACT 2600,Australia
国际会议
澳门
英文
526-538
2019-04-14（万方平台首次上网日期，不代表论文的发表时间）