Cost-effective Variational Active Entity Resolution

TL;DR

This paper introduces a cost-effective entity resolution method leveraging deep autoencoders, unsupervised learning, transfer learning, and active learning to reduce human effort and training costs while maintaining high accuracy.

Contribution

It presents a novel approach combining deep autoencoders, transfer learning, and active learning to lower costs in entity resolution without sacrificing effectiveness.

Findings

Achieves comparable accuracy to state-of-the-art methods.

Reduces labeling and training costs significantly.

Demonstrates transferability of autoencoder-based models.

Abstract

Accurately identifying different representations of the same real-world entity is an integral part of data cleaning and many methods have been proposed to accomplish it. The challenges of this entity resolution task that demand so much research attention are often rooted in the task-specificity and user-dependence of the process. Adopting deep learning techniques has the potential to lessen these challenges. In this paper, we set out to devise an entity resolution method that builds on the robustness conferred by deep autoencoders to reduce human-involvement costs. Specifically, we reduce the cost of training deep entity resolution models by performing unsupervised representation learning. This unveils a transferability property of the resulting model that can further reduce the cost of applying the approach to new datasets by means of transfer learning. Finally, we reduce the cost of labelling training data through an active learning approach that builds on the properties conferred by the use of deep autoencoders. Empirical evaluation confirms the accomplishment of our cost-reduction desideratum while achieving comparable effectiveness with state-of-the-art alternatives.