Bayesian Graphical Entity Resolution Using Exchangeable Random Partition Priors

TL;DR

This paper introduces a Bayesian graphical model for entity resolution that uses exchangeable priors, a realistic distortion model, hyperpriors, and a faster inference algorithm, demonstrating improved accuracy across various datasets.

Contribution

It presents a novel Bayesian approach with exchangeable priors, a refined distortion model, hyperpriors, and an efficient Gibbs sampler for entity resolution.

Findings

Model outperforms alternatives in accuracy

Consistent performance across diverse scenarios

Higher F1 scores in experiments

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.