  Databases often contain corrupted, degraded, and noisy data with duplicate
entries across and within each database. Such problems arise in citations,
medical databases, genetics, human rights databases, and a variety of other
applied settings. The target of statistical inference can be viewed as an
unsupervised problem of determining the edges of a bipartite graph that links
the observed records to unobserved latent entities. Bayesian approaches provide
attractive benefits, naturally providing uncertainty quantification via
posterior probabilities. We propose a novel record linkage approach based on
empirical Bayesian principles. Specifically, the empirical Bayesian--type step
consists of taking the empirical distribution function of the data as the prior
for the latent entities. This approach improves on the earlier HB approach not
only by avoiding the prior specification problem but also by allowing both
categorical and string-valued variables. Our extension to string-valued
variables also involves the proposal of a new probabilistic mechanism by which
observed record values for string fields can deviate from the values of their
associated latent entities. Categorical fields that deviate from their
corresponding true value are simply drawn from the empirical distribution
function. We apply our proposed methodology to a simulated data set of German
names and an Italian household survey, showing our method performs favorably
compared to several standard methods in the literature. We also consider the
robustness of our methods to changes in the hyper-parameters.
