Domain Adaptation for Deep Entity Resolution: A Design Space Exploration
Entity resolution (ER) is a core problem of data integration. The state-of-the-art (SOTA) results on ER are achieved by deep learning (DL) based methods, trained with a lot of labeled matching/non-matching entity pairs. This may not be a problem when using well-prepared benchmark datasets. Nevertheless, for many real-world ER applications, the situation changes dramatically, with a painful issue to collect large-scale labeled datasets. In this paper, we seek to answer: If we have a well-labeled source ER dataset, can we train a DL-based ER model for a target dataset, without any labels or with a few labels? This is known as domain adaptation (DA), which has achieved great successes in computer vision and natural language processing, but is not systematically studied for ER. Our goal is to systematically explore the benefits and limitations of a wide range of DA methods for ER. To this purpose, we develop a DADER (Domain Adaptation for Deep Entity Resolution) framework that significantly advances ER in applying DA. We define a space of design solutions for the three modules of DADER, namely Feature Extractor, Matcher, and Feature Aligner. We conduct so far the most comprehensive experimental study to explore the design space and compare different choices of DA for ER. We provide guidance for selecting appropriate design solutions based on extensive experiments.
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