  Usually considered as a classification problem, entity resolution (ER) can be
very challenging on real data due to the prevalence of dirty values. The
state-of-the-art solutions for ER were built on a variety of learning models
(most notably deep neural networks), which require lots of accurately labeled
training data. Unfortunately, high-quality labeled data usually require
expensive manual work, and are therefore not readily available in many real
scenarios. In this paper, we propose a novel learning paradigm for ER, called
gradual machine learning, which aims to enable effective machine labeling
without the requirement for manual labeling effort. It begins with some easy
instances in a task, which can be automatically labeled by the machine with
high accuracy, and then gradually labels more challenging instances by
iterative factor graph inference. In gradual machine learning, the hard
instances in a task are gradually labeled in small stages based on the
estimated evidential certainty provided by the labeled easier instances. Our
extensive experiments on real data have shown that the performance of the
proposed approach is considerably better than its unsupervised alternatives,
and highly competitive compared to the state-of-the-art supervised techniques.
Using ER as a test case, we demonstrate that gradual machine learning is a
promising paradigm potentially applicable to other challenging classification
tasks requiring extensive labeling effort.
