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Combining Context and Existing Knowledge When Recognizing Biological Entities – Early Results

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Advances in Knowledge Discovery and Data Mining (PAKDD 2008)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5012))

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Abstract

Entity recognition has been studied for several years with good results. However, as the focus of information extraction (IE) and entity recognition (ER) has been set on biology and bioinformatics, the existing methods do not produce as good results as before. This is mainly due to the complex naming conventions of biological entities. In our information extraction system for biomedical documents called OAT (Ontology Aided Text mining system) we developed our own method for recognizing the biological entities. The difference to the existing methods, which use lexicons, rules and statistics, is that we combine the context of the entity with the existing knowledge about the relationships of the entities. This has produced encouraging preliminary results. This paper describes the approach we are using in our information extraction system for entity recognition.

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Authors and Affiliations

  1. VTT, Box 1000, FI-02044 VTT, Finland

    Mika Timonen & Antti Pesonen

Authors
  1. Mika Timonen
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  2. Antti Pesonen
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Takashi Washio Einoshin Suzuki Kai Ming Ting Akihiro Inokuchi

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© 2008 Springer-Verlag Berlin Heidelberg

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Timonen, M., Pesonen, A. (2008). Combining Context and Existing Knowledge When Recognizing Biological Entities – Early Results. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2008. Lecture Notes in Computer Science(), vol 5012. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68125-0_109

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  • DOI: https://doi.org/10.1007/978-3-540-68125-0_109

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-68124-3

  • Online ISBN: 978-3-540-68125-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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