The merge/purge problem for large databases
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Efficient clustering of high-dimensional data sets with application to reference matching
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Adaptive duplicate detection using learnable string similarity measures
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A hierarchical graphical model for record linkage
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Adaptive Product Normalization: Using Online Learning for Record Linkage in Comparison Shopping
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Adaptive Name Matching in Information Integration
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Efficiently linking text documents with relevant structured information
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Duplicate Record Detection: A Survey
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Auditing compliance with a Hippocratic database
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ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
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EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Structured annotations of web queries
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An e-commerce catalog typically comprises of specifications for millions of products. The search engine receives millions of sales offers from thousands of independent merchants that must be matched to the right products. We describe the challenges that a system for matching unstructured offers to structured product descriptions must address, drawing upon our experience from building such a system for Bing Shopping. The heart of our system is a data-driven component that learns the matching function off-line, which is then applied at run-time for matching offers to products. We provide the design of this and other critical components of the system as well as the details of the extensive experiments we performed to assess the readiness of the system. This system is currently deployed in an experimental Commerce Search Engine and is used to match all the offers received by Bing Shopping to the Bing product catalog.