Information Integration of Partially Labeled Data
A central task when integrating data from different sources is to detect identical items. For example, price comparison websites have to identify offers for identical products. This task is known, among others, as record linkage, object identification, or duplicate detection.
In this work, we examine problem settings where some relations between items are given in advance — for example by EAN article codes in an e-commerce scenario or by manually labeled parts. To represent and solve these problems we bring in ideas of semi-supervised and constrained clustering in terms of pairwise must-link and cannot-link constraints. We show that extending object identification by pairwise constraints results in an expressive framework that subsumes many variants of the integration problem like traditional object identification, matching, iterative problems or an active learning setting.
For solving these integration tasks, we propose an extension to current object identification models that assures consistent solutions to problems with constraints. Our evaluation shows that additionally taking the labeled data into account dramatically increases the quality of state-of-the-art object identification systems.
KeywordsInformation Integration Record Linkage Consistent Solution Pairwise Constraint Levenshtein Distance
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