NDLI: Exploring a Few Good Tuples from Text Databases

Content Provider	IEEE Xplore Digital Library
Author	Jain, A. Srivastava, D.
Copyright Year	2009
Abstract	Information extraction from text databases is a useful paradigm to populate relational tables and unlock the considerable value hidden in plain-text documents. However, information extraction can be expensive, due to various complex text processing steps necessary in uncovering the hidden data. There are a large number of text databases available, and not every text database is necessarily relevant to every relation. Hence, it is important to be able to quickly explore the utility of running an extractor for a specific relation over a given text database before carrying out the expensive extraction task. In this paper, we present a novel exploration methodology of {\em finding a few good tuples} for a relation that can be extracted from a database which allows for judging the relevance of the database for the relation. Specifically, we propose the notion of a good(k, $\ell$) query as one that can return any $k$ tuples for a relation among the top-$\ell$ fraction of tuples ranked by their aggregated confidence scores, provided by the extractor; if these tuples have high scores, the database can be determined as relevant to the relation. We formalize the access model for information extraction, and investigate efficient query processing algorithms for good(k, $\ell$) queries, which do not rely on any prior knowledge about the extraction task or the database. We demonstrate the viability of our algorithms using a detailed experimental study with real text databases.
Starting Page	616
Ending Page	627
File Size	591859
Page Count	12
File Format	PDF
ISBN	9781424434220
ISSN	10844627
DOI	10.1109/ICDE.2009.120
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-03-29
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data mining Diseases Relational databases USA Councils Pattern matching Text processing Data engineering Query processing Electronic mail Avatars text databases Information extraction query processing
Content Type	Text
Resource Type	Article
Subject	Signal Processing Information Systems Software

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