Bayesian Graphical Entity Resolution using Exchangeable Random Partition Priors
Author:
Marchant Neil G1ORCID,
Rubinstein Benjamin I P2,
Steorts Rebecca C3
Affiliation:
1. School of Computing and Information Systems, University of Melbourne Research Fellow in the , Australia
2. School of Computing and Information Systems, University of Melbourne Professor in the , Australia
3. Department of Statistical Science, Duke University Assistant Professor in the , USA
Abstract
Abstract
Entity resolution (record linkage or deduplication) is the process of identifying and linking duplicate records in databases. In this paper, we propose a Bayesian graphical approach for entity resolution that links records to latent entities, where the prior representation on the linkage structure is exchangeable. First, we adopt a flexible and tractable set of priors for the linkage structure, which corresponds to a special class of random partition models. Second, we propose a more realistic distortion model for categorical/discrete record attributes, which corrects a logical inconsistency with the standard hit-miss model. Third, we incorporate hyperpriors to improve flexibility. Fourth, we employ a partially collapsed Gibbs sampler for inferential speedups. Using a selection of private and nonprivate data sets, we investigate the impact of our modeling contributions and compare our model with two alternative Bayesian models. In addition, we conduct a simulation study for household survey data, where we vary distortion, duplication rates and data set size. We find that our model performs more consistently than the alternatives across a variety of scenarios and typically achieves the highest entity resolution accuracy (F1 score). Open source software is available for our proposed methodology, and we provide a discussion regarding our work and future directions.
Funder
National Science Foundation
Australian Research Council
Australian Government Research Training Program
Publisher
Oxford University Press (OUP)
Subject
Applied Mathematics,Statistics, Probability and Uncertainty,Social Sciences (miscellaneous),Statistics and Probability
Reference48 articles.
1. (Almost) All of Entity Resolution;Binette;Science Advances,2022
2. Handbook of Markov Chain Monte Carlo
3. Understanding the Metropolis-Hastings Algorithm;Chib;The American Statistician,1995
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