Incremental information extraction using relational databases

Luis Tari, Phan Huy Tu, Jörg Hakenberg, Yi Chen, Tran Cao Son, Graciela Gonzalez, Chitta Baral

Research output: Contribution to journalArticle

20 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number5611526
Pages (from-to)86-99
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Volume24
Issue number1
DOIs
StatePublished - Jan 1 2012

Keywords

  • Text mining
  • information storage and retrieval
  • query languages

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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  • Cite this

    Tari, L., Tu, P. H., Hakenberg, J., Chen, Y., Son, T. C., Gonzalez, G., & Baral, C. (2012). Incremental information extraction using relational databases. IEEE Transactions on Knowledge and Data Engineering, 24(1), 86-99. [5611526]. https://doi.org/10.1109/TKDE.2010.214