  Entity matching (EM) refers to the problem of identifying pairs of data
records in one or more relational tables that refer to the same entity in the
real world. Supervised machine learning (ML) models currently achieve
state-of-the-art matching performance; however, they require many labeled
examples, which are often expensive or infeasible to obtain. This has inspired
us to approach data labeling for EM using weak supervision. In particular, we
use the labeling function abstraction popularized by Snorkel, where each
labeling function (LF) is a user-provided program that can generate many noisy
match/non-match labels quickly and cheaply. Given a set of user-written LFs,
the quality of data labeling depends on a labeling model to accurately infer
the ground-truth labels. In this work, we first propose a simple but powerful
labeling model for general weak supervision tasks. Then, we tailor the labeling
model specifically to the task of entity matching by considering the
EM-specific transitivity property.
  The general form of our labeling model is simple while substantially
outperforming the best existing method across ten general weak supervision
datasets. To tailor the labeling model for EM, we formulate an approach to
ensure that the final predictions of the labeling model satisfy the
transitivity property required in EM, utilizing an exact solution where
possible and an ML-based approximation in remaining cases. On two single-table
and nine two-table real-world EM datasets, we show that our labeling model
results in a 9% higher F1 score on average than the best existing method. We
also show that a deep learning EM end model (DeepMatcher) trained on labels
generated from our weak supervision approach is comparable to an end model
trained using tens of thousands of ground-truth labels, demonstrating that our
approach can significantly reduce the labeling efforts required in EM.
