  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.
