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Incremental Information Extraction Using Relational Databases
January 2012 (vol. 24 no. 1)
pp. 86-99
ASCII Text
x 
 
Luis Tari, Phan Huy Tu, Jörg Hakenberg, Yi Chen, Tran Cao Son, Graciela Gonzalez, Chitta Baral, "Incremental Information Extraction Using Relational Databases," IEEE Transactions on Knowledge and Data Engineering, vol. 24, no. 1, pp. 86-99, January, 2012.
 
 
BibTex
x
 
@article{ 10.1109/TKDE.2010.214,
author = {Luis Tari and Phan Huy Tu and Jörg Hakenberg and Yi Chen and Tran Cao Son and Graciela Gonzalez and Chitta Baral},
title = {Incremental Information Extraction Using Relational Databases},
journal ={IEEE Transactions on Knowledge and Data Engineering},
volume = {24},
number = {1},
issn = {1041-4347},
year = {2012},
pages = {86-99},
doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2010.214},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Knowledge and Data Engineering
TI - Incremental Information Extraction Using Relational Databases
IS - 1
SN - 1041-4347
SP86
EP99
EPD - 86-99
A1 - Luis Tari,
A1 - Phan Huy Tu,
A1 - Jörg Hakenberg,
A1 - Yi Chen,
A1 - Tran Cao Son,
A1 - Graciela Gonzalez,
A1 - Chitta Baral,
PY - 2012
KW - Text mining
KW - query languages
KW - information storage and retrieval.
VL - 24
JA - IEEE Transactions on Knowledge and Data Engineering
ER -
 
Luis Tari, Arizona State University, Tempe
Phan Huy Tu, Arizona State University, Tempe
Jörg Hakenberg, Arizona State University, Tempe
Yi Chen, Arizona State University, Tempe
Tran Cao Son, New Mexico State University, Las Cruces
Graciela Gonzalez, Arizona State University, Tempe
Chitta Baral, Arizona State University, Tempe
Information extraction systems are traditionally implemented as a pipeline of special-purpose processing modules targeting the extraction of a particular kind of information. A major drawback of such an approach is that whenever a new extraction goal emerges or a module is improved, extraction has to be reapplied from scratch to the entire text corpus even though only a small part of the corpus might be affected. In this paper, we describe a novel approach for information extraction in which extraction needs are expressed in the form of database queries, which are evaluated and optimized by database systems. Using database queries for information extraction enables generic extraction and minimizes reprocessing of data by performing incremental extraction to identify which part of the data is affected by the change of components or goals. Furthermore, our approach provides automated query generation components so that casual users do not have to learn the query language in order to perform extraction. To demonstrate the feasibility of our incremental extraction approach, we performed experiments to highlight two important aspects of an information extraction system: efficiency and quality of extraction results. Our experiments show that in the event of deployment of a new module, our incremental extraction approach reduces the processing time by 89.64 percent as compared to a traditional pipeline approach. By applying our methods to a corpus of 17 million biomedical abstracts, our experiments show that the query performance is efficient for real-time applications. Our experiments also revealed that our approach achieves high quality extraction results.

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Index Terms:
Text mining, query languages, information storage and retrieval.
Citation:
Luis Tari, Phan Huy Tu, Jörg Hakenberg, Yi Chen, Tran Cao Son, Graciela Gonzalez, Chitta Baral, "Incremental Information Extraction Using Relational Databases," IEEE Transactions on Knowledge and Data Engineering, vol. 24, no. 1, pp. 86-99, Jan. 2012, doi:10.1109/TKDE.2010.214
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