Abstract
Entity matching (EM) is crucial step in data integration. Supervised machine learning (SML) approaches have attained the SOTA performance in EM. In real - world scenarios SML suffers from absence or lack of large labeled data for training. Active machine learning (AML) for EM minimize the number of training data required and tries to reduce the amount of Hand labeling by picking just the helpful pairs. ALL AML approaches use just one of the two criteria - informativeness or representativeness - for query selection, Which limit their efficacy. In this work, we propose a Combined Score Sampling (CSS) that combines informativeness and representativeness selection criteria. We evaluate the CSS using the benchmark e-commerce data-sets pair Abt-Buy and AML with ensemble learning as SML model and we demonstrate that it effectively addresses the issue. Comparing our strategy to SML, we demonstrate that it lead to overall enhanced F1 score and stability of the learnt models.
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Jabrane, M., Hafidi, I., Rochd, Y. (2023). An Improved Active Machine Learning Query Strategy for Entity Matching Problem. In: Aboutabit, N., Lazaar, M., Hafidi, I. (eds) Advances in Machine Intelligence and Computer Science Applications. ICMICSA 2022. Lecture Notes in Networks and Systems, vol 656. Springer, Cham. https://doi.org/10.1007/978-3-031-29313-9_28
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