Heterogeneous Committee-Based Active Learning for Entity Resolution (HeALER)

Entity resolution identifies records that refer to the same real-world entity. For its classification step, supervised learning can be adopted, but this faces limitations in the availability of labeled training data. Under this situation, active learning has been proposed to gather labels while reducing the human labeling effort, by selecting the most informative data as candidates for labeling. Committee-based active learning is one of the most commonly used approaches, which chooses data with the most disagreement of voting results of the committee, considering this as the most informative data. However, the current state-of-the-art committee-based active learning approaches for entity resolution have two main drawbacks: First, the selected initial training data is usually not balanced and informative enough. Second, the committee is formed with homogeneous classifiers by comprising their accuracy to achieve diversity of the committee, i.e., the classifiers are not trained with all available training data or the best parameter setting. In this paper, we propose our committee-based active learning approach HeALER, which overcomes both drawbacks by using more effective initial training data selection approaches and a more effective heterogenous committee. We implemented HeALER and compared it with passive learning and other state-of-the-art approaches. The experiment results prove that our approach outperforms other state-of-the-art committee-based active learning approaches.