Dual-Module Feature Alignment Domain Adversarial Model for Entity Resolution

Entity Resolution (ER) is a fundamental task in data integration, aiming to identify data objects across different sources that refer to the same real-world entity. In recent years, deep learning solutions have become the mainstream approach. However, their effectiveness often depends on the availability of large amounts of labeled training data, which is often challenging to obtain in many scenarios. To address this challenge, we propose utilizing Domain Adversarial Neural Networks (DANN) for efficient domain adaptation, transferring knowledge from labeled source ER datasets to new target ER datasets. However, domain adaptation for ER faces unique challenges: (1) it requires transferring knowledge or parameters from individual records, not just considering the similarity of record pairs; (2) there is significant class imbalance between positive and negative samples, affecting the model's generalization ability and accuracy; (3) in DANN, adjusting the classifier solely using labeled data from the source domain leads to features biased towards the source domain. To address these challenges, we propose a model called the Dual-Module Feature Alignment Domain Adversarial Entity Resolution (DFA-ER). DFA-ER combines a feature extractor based on relationship reconstruction and a dual-classifier domain adversarial network optimized with the Maximize Classifier Difference (MCD) strategy, to enhance applicability to ER tasks. DFA-ER adopts a dual-module network architecture and uses adversarial training to align the feature distribution of entity record pairs in different domains from the perspectives of domain invariant features and domain discriminant features, thereby improving the performance of target domain tasks. Experimental results show that, compared to baseline methods, DFA-ER achieves better performance across various dataset settings.