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Author Ratner, Alex ♦ Wang, Feiran ♦ Wu, Sen ♦ Zhang, Ce ♦ Cafarella, Michael ♦ Shin, Jaeho ♦ Ré, Christopher ♦ De Sa, Christopher
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Language English
Abstract The dark data extraction or knowledge base construction (KBC) problem is to populate a relational database with information from unstructured data sources, such as emails, webpages, and PDFs. KBC is a long-standing problem in industry and research that encompasses problems of data extraction, cleaning, and integration. We describe DeepDive, a system that combines database and machine learning ideas to help to develop KBC systems. The key idea in DeepDive is to frame traditional extract-transform-load (ETL) style data management problems as a single large statistical inference task that is declaratively defined by the user. DeepDive leverages the effectiveness and efficiency of statistical inference and machine learning for difficult extraction tasks, whereas not requiring users to directly write any probabilistic inference algorithms. Instead, domain experts interact with DeepDive by defining features or rules about the domain. DeepDive has been successfully applied to domains such as pharmacogenomics, paleobiology, and antihuman trafficking enforcement, achieving human-caliber quality at machine-caliber scale. We present the applications, abstractions, and techniques used in DeepDive to accelerate the construction of such dark data extraction systems.
Description Affiliation: Lattice Data, Inc., Palo Alto, CA (Cafarella, Michael; Shin, Jaeho) || Stanford University, Stanford, CA (Ré, Christopher; De Sa, Christopher; Ratner, Alex; Wang, Feiran; Wu, Sen) || ETH Zurich, Zurich, Switzerland (Zhang, Ce)
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2005-08-01
Publisher Place New York
Journal Communications of the ACM (CACM)
Volume Number 60
Issue Number 5
Page Count 10
Starting Page 93
Ending Page 102

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Source: ACM Digital Library