Text-based knowledge discovery: search and mining of life-sciences documents.
Text literature is playing an increasingly important role in biomedical discovery. The challenge is to manage the increasing volume, complexity and specialization of knowledge expressed in this literature. Although information retrieval or text searching is useful, it is not sufficient to find specific facts and relations. Information extraction methods are evolving to extract automatically specific, fine-grained terms corresponding to the names of entities referred to in the text, and the relationships that connect these terms. Information extraction is, in turn, a means to an end, and knowledge discovery methods are evolving for the discovery of still more-complex structures and connections among facts. These methods provide an interpretive context for understanding the meaning of biological data.
Mack
Robert
R
IBM, Thomas J. Watson Research Centre, 19 Skyline Drive, Hawthorne, NY 10532, USA.
Hehenberger
Michael
M
D001690:Biological Science Disciplines; D019295:Computational Biology / Q000379:methods*; D057225:Data Mining; D003628:Database Management Systems; D016206:Databases, Bibliographic; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D009323:Natural Language Processing; D011642:Publications