  The goal of entity matching is to find the corresponding records representing
the same real-world entity from different data sources. At present, in the
mainstream methods, rule-based entity matching methods need tremendous domain
knowledge. The machine-learning based or deep-learning based entity matching
methods need a large number of labeled samples to build the model, which is
difficult to achieve in some applications. In addition, learning-based methods
are easy to over-fitting, so the quality requirements of training samples are
very high. In this paper, we present an active learning method ALMatcher for
the entity matching tasks. This method needs to manually label only a small
number of valuable samples, and use these samples to build a model with high
quality. This paper proposes a hybrid uncertainty as query strategy to find
those valuable samples for labeling, which can minimize the number of labeled
training samples meanwhile meet the task requirements. The proposed method has
been validated on seven data sets in different fields. The experiment shows
that ALMatcher uses only a small number of labeled samples and achieves better
results compared to existing approaches.
