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  \textbf{\Large{\textsc{STANFORD UNIVERSITY}}}\\[5pt]
  \textbf{\Large{\textsc{DEPARTMENT OF STATISTICS}}}\\[5pt]
  \Large{\textsc{DEPARTMENTAL SEMINAR}}
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  4:15 p.m., Tuesday, August 7, 2007\\
  Sequoia Hall Room 200\\
  (Cookies at 3:45 in 1st Floor Lounge)
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  \textsl{Balaji S. Srinivasan} \\
  Department of Statistics\\
  Stanford University
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  \subsection*{Automatic Population of Biomedical Ontologies}
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\noindent Biomedical classification systems like the Gene Ontology
(GO) have proven invaluable for converting disordered collections of
free text into machine-readable knowledge representations. However,
the scalability of these ontologies is currently limited because they
are populated manually from the literature at great expense. Here, we
present an algorithm which removes this limitation by automatically
extracting ontological relationships between objects from a massive
corpus of more than 425000 full text biomedical articles. 

Given a small training set of biological objects with known
relationships such as ``\verb+is_a+'', ``\verb+localized_to+'', or
``\verb+regulates_a+'', our algorithm finds the lexico-syntactic
patterns which specify this relationship in plain text. These learned
patterns can then be used to find many more examples of objects that
satisfy these relationships, thereby automatically populating an
ontology. As a case in point, we show the results of applying the
algorithm to locate text snippets specifying gene localizations,
taxonomic relationships, and anatomical connections. Our methods
greatly reduce the amount of manual curator effort, thereby allowing
ontological modeling to scale.
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