The merge/purge problem for large databases
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The Throughput of Sequential Testing
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WebTables: exploring the power of tables on the web
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Iterative Set Expansion of Named Entities Using the Web
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Table extraction using spatial reasoning on the CSS2 visual box model
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Unsupervised named-entity extraction from the Web: An experimental study
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Data integration for the relational web
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Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
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Proceedings of the VLDB Endowment
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A bottom-up, knowledge-aware approach to integrating and querying web data services
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Synthesizing union tables from the web
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We consider the problem of finding related tables in a large corpus of heterogenous tables. Detecting related tables provides users a powerful tool for enhancing their tables with additional data and enables effective reuse of available public data. Our first contribution is a framework that captures several types of relatedness, including tables that are candidates for joins and tables that are candidates for union. Our second contribution is a set of algorithms for detecting related tables that can be either unioned or joined. We describe a set of experiments that demonstrate that our algorithms produce highly related tables. We also show that we can often improve the results of table search by pulling up tables that are ranked much lower based on their relatedness to top-ranked tables. Finally, we describe how to scale up our algorithms and show the results of running it on a corpus of over a million tables extracted from Wikipedia.