Entity Matching by Pool-based Active Learning
This addresses the challenge of high labeling costs and overfitting in entity matching for data integration applications, though it is incremental as it builds on active learning techniques.
The paper tackles the problem of entity matching, where existing methods require extensive domain knowledge or large labeled datasets, by proposing ALMatcher, an active learning method that uses a hybrid uncertainty query strategy to reduce labeling effort. The method was validated on seven datasets, achieving better results with only a small number of labeled samples compared to existing approaches.
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.