Finding Explicit and Implicit Knowledge: Biomedical Text Data Mining

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Author(s): Kazuhiro Seki (Kobe University, Japan), Javed Mostafa (University of North Carolina at Chapel Hill, USA)and Kuniaki Uehara (Kobe University, Japan)
Copyright: 2010
Pages: 17
Source title: Intelligent Soft Computation and Evolving Data Mining: Integrating Advanced Technologies
Source Author(s)/Editor(s): Leon Shyue-Liang Wang (National University of Kaohsiung, Taiwan)and Tzung-Pei Hong (National University of Kaohsiung, Taiwan)
DOI: 10.4018/978-1-61520-757-2.ch017

Keywords: Data Mining / Data Mining and Databases / Information Science Reference / Library & Information Science

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Abstract

This chapter discusses two different types of text data mining focusing on the biomedical literature. One deals with explicit information or facts written in articles, and the other targets implicit information or hypotheses inferred from explicit information. A major difference between the two is that the former is bound to the contents within the literature, whereas the latter goes beyond existing knowledge and generates potential scientific hypotheses. As concrete examples applied to real-world problems, this chapter looks at two applications of text data mining: gene functional annotation and genetic association discovery, both considered to have significant practical importance.

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