A novel ensemble learning approach to unsupervised record linkage

Jurek, Anna; Hong, Jun; Chi, Yuan; Liu, Weiru

Abstract

© 2017 Record linkage is a process of identifying records that refer to the same real-world entity. Many existing approaches to record linkage apply supervised machine learning techniques to generate a classification model that classifies a pair of records as either match or non-match. The main requirement of such an approach is a labelled training dataset. In many real-world applications no labelled dataset is available hence manual labelling is required to create a sufficiently sized training dataset for a supervised machine learning algorithm. Semi-supervised machine learning techniques, such as self-learning or active learning, which require only a small manually labelled training dataset have been applied to record linkage. These techniques reduce the requirement on the manual labelling of the training dataset. However, they have yet to achieve a level of accuracy similar to that of supervised learning techniques. In this paper we propose a new approach to unsupervised record linkage based on a combination of ensemble learning and enhanced automatic self-learning. In the proposed approach an ensemble of automatic self-learning models is generated with different similarity measure schemes. In order to further improve the automatic self-learning process we incorporate field weighting into the automatic seed selection for each of the self-learning models. We propose an unsupervised diversity measure to ensure that there is high diversity among the selected self-learning models. Finally, we propose to use the contribution ratios of self-learning models to remove those with poor accuracy from the ensemble. We have evaluated our approach on 4 publicly available datasets which are commonly used in the record linkage community. Our experimental results show that our proposed approach has advantages over the state-of-the-art semi-supervised and unsupervised record linkage techniques. In 3 out of 4 datasets it also achieves comparable results to those of the supervised approaches.

• Journal Article Type: Article
• Acceptance Date: Jun 27, 2017
• Online Publication Date: Jun 28, 2017
• Publication Date: Nov 1, 2017
• Deposit Date: Jul 10, 2017
• Publicly Available Date: Jun 28, 2018
• Journal: Information Systems
• Print ISSN: 0306-4379
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 71
• Pages: 40-54
• DOI: https://doi.org/10.1016/j.is.2017.06.006
• Keywords: unsupervised record linkage, data matching, classification, ensemble learning
• Public URL: https://uwe-repository.worktribe.com/output/877934
• Publisher URL: https://doi.org/10.1016/j.is.2017.06.006
• Related Public URLs: http://www.sciencedirect.com/science/article/pii/S0306437916305063
• Contract Date: Jul 10, 2017