GenerIE: Information extraction using database queries

Information extraction systems are traditionally implemented as a pipeline of special-purpose processing modules. A major drawback of such an approach is that whenever a new extraction goal emerges or a module is improved, extraction has to be re-applied from scratch to the entire text corpus even t...

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Bibliographic Details
Main Authors: Tari, Luis, Tu, Phan Huy, Hakenberg, Jorg, Chen, Yi, Son, Tran Cao, Gonzalez, Graciela, Baral, Chitta
Format: Conference Proceeding
Language:English
Subjects:
Biomedical engineering
Biomedical informatics
Computer science
Data engineering
Data mining
Database languages
Pipelines
Proposals
Tagging
Text processing
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Summary:Information extraction systems are traditionally implemented as a pipeline of special-purpose processing modules. A major drawback of such an approach is that whenever a new extraction goal emerges or a module is improved, extraction has to be re-applied from scratch to the entire text corpus even though only a small part of the corpus might be affected. In this demonstration proposal, we describe a novel paradigm for information extraction: we store the parse trees output by text processing in a database, and then express extraction needs using queries, which can be evaluated and optimized by databases. Compared with the existing approaches, database queries for information extraction enable generic extraction and minimize reprocessing. However, such an approach also poses a lot of technical challenges, such as language design, optimization and automatic query generation. We will present the opportunities and challenges that we met when building GenerIE, a system that implements this paradigm.
ISSN:1063-6382
DOI:10.1109/ICDE.2010.5447773