Decision Models for Record Linkage

Abstract

The process of identifying record pairs that represent the same real-world entity in multiple databases, commonly known as record linkage, is one of the important steps in many data mining applications. In this paper, we address one of the sub-tasks in record linkage, i.e., the problem of assigning record pairs with an appropriate matching status. Techniques for solving this problem are referred to as decision models. Most existing decision models rely on good training data, which is, however, not commonly available in real-world applications. Decision models based on unsupervised machine learning techniques have recently been proposed. In this paper, we review several existing decision models and then propose an enhancement to cluster-based decision models. Experimental results show that our proposed decision model achieves the same accuracy of existing models while significantly reducing the number of record pairs required for manual review. The proposed model also provides a mechanism to trade off the accuracy with the number of record pairs required for clerical review.