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Automatic construction of knowledge base from biological papers

Y Ohta1, Y Yamamoto, T Okazaki

  • 1Human Genome Center, University of Tokyo, Japan. yoh@ims.u-tokyo.ac.jp

Proceedings. International Conference on Intelligent Systems for Molecular Biology
|January 1, 1997
PubMed
Summary

This study introduces the Information Finding from Biological Papers (IFBP) system to automatically extract domain-specific knowledge from biological literature. IFBP efficiently builds knowledge bases, like the Transcription Factor DataBase (TFDB), with potential for broad application.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Knowledge Discovery

Background:

  • Automated knowledge acquisition from scientific literature is crucial for biological research.
  • Existing methods may lack efficiency or broad applicability across different biological domains.

Purpose of the Study:

  • To develop an automated system, IFBP, for extracting domain-specific knowledge from biological papers.
  • To construct a specialized dictionary and improve information retrieval and extraction processes.

Main Methods:

  • IFBP employs a three-phase approach: Information Retrieval (IR), Information Extraction (IE), and Dictionary Construction (DC).
  • Utilizes a query modification method with an automatically built thesaurus for IR.
  • Implements a statistical keyword prediction method for IE and automatic dictionary construction.

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Main Results:

  • The IFBP system demonstrates good performance in constructing the Transcription Factor DataBase (TFDB).
  • The automated nature of the knowledge base construction model facilitates easy porting to new domains.

Conclusions:

  • IFBP offers an effective and automated solution for knowledge acquisition from biological texts.
  • The system's design allows for flexible adaptation to various biological research areas, accelerating knowledge discovery.