A Hierarchical Graphical Model for Record Linkage

The task of matching co-referent records is known among other names as record linkage. For large record-linkage problems, often there is little or no labeled data available, but unlabeled data shows a reasonably clear structure. For such problems, unsupervised or semi-supervised methods are preferable to supervised methods. In this paper, we describe a hierarchical graphical model framework for the record-linkage problem in an unsupervised setting. In addition to proposing new methods, we also cast existing unsupervised probabilistic record-linkage methods in this framework. Some of the techniques we propose to minimize overfitting in the above model are of interest in the general graphical model setting. We describe a method for incorporating monotonicity constraints in a graphical model. We also outline a bootstrapping approach of using "singlefield" classifiers to noisily label latent variables in a hierarchical model. Experimental results show that our proposed unsupervi...

• Type: Conference
• Year: 2004
• Where: UAI
• Authors: Pradeep D. Ravikumar, William W. Cohen
• Keywords: Graphical Model, Hierarchical Graphical Model, UAI 2004, UAI 2008, Unsupervised Probabilistic Record-linkage