2017 DeepDiveDeclarativeKnowledgeBas

• (Zhang et al., 2017) ⇒ Ce Zhang, Christopher Ré, Michael Cafarella, Christopher De Sa, Alex Ratner, Jaeho Shin, Feiran Wang, and Sen Wu. (2017). “DeepDive: Declarative Knowledge Base Construction.” In: Communications of the ACM Journal, 60(5). doi:10.1145/3060586

Subject Headings: DeepDive System.

Cited By

• http://scholar.google.com/scholar?q=%222017%22+DeepDive%3A+Declarative+Knowledge+Base+Construction
• http://dl.acm.org/citation.cfm?id=3084186.3060586&preflayout=flat#citedby

Quotes

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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2017 DeepDiveDeclarativeKnowledgeBas	Michael J. Cafarella Sen Wu Ce Zhang Jaeho Shin Feiran Wang Christopher De Sa Alex Ratner Christopher Ré			DeepDive: Declarative Knowledge Base Construction				10.1145/3060586		2017