2012/07/12 by Pradeep Ravikumar, William W. Cohen, Ravikumar, Pradeep +1 · 1 citation
Computer Science · Decision Sciences · #Advanced Database Systems and Queries #Data Quality and Management #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Semantic Web and Ontologies
paper · pdf · doi:10.48550/arxiv.1207.4180
openalex publication_date 2012/07/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The task of matching co-referent records is known among other names as rocord linkage. For large record-linkage problems, often there is little or no labeled data available, but unlabeled data shows a reasonable 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 linakge-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 monotinicity constraints in a graphical model. We also outline a bootstrapping approach of using "single-field" classifiers to noisily label latent variables in a hierarchical model. Experimental results show that our proposed unsupervised methods perform quite competitively even with fully supervised record-linkage methods.