New challenges for text mining: mapping between text and manually curated pathways.
BACKGROUND
Associating literature with pathways poses new challenges to the Text Mining (TM) community. There are three main challenges to this task: (1) the identification of the mapping position of a specific entity or reaction in a given pathway, (2) the recognition of the causal relationships among multiple reactions, and (3) the formulation and implementation of required inferences based on biological domain knowledge.


RESULTS
To address these challenges, we constructed new resources to link the text with a model pathway; they are: the GENIA pathway corpus with event annotation and NF-kB pathway. Through their detailed analysis, we address the untapped resource, 'bio-inference,' as well as the differences between text and pathway representation. Here, we show the precise comparisons of their representations and the nine classes of 'bio-inference' schemes observed in the pathway corpus.


CONCLUSIONS
We believe that the creation of such rich resources and their detailed analysis is the significant first step for accelerating the research of the automatic construction of pathway from text.
Oda
Kanae
K
Department of Computer Science, Graduate School of Information Science and Technology, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan. k.oda@is.s.u-tokyo.ac.jp
Kim
Jin-Dong
JD
Ohta
Tomoko
T
Okanohara
Daisuke
D
Matsuzaki
Takuya
T
Tateisi
Yuka
Y
Tsujii
Jun'ichi
J
D000465:Algorithms; D001185:Artificial Intelligence; D004014:Dictionaries as Topic; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods*; D010506:Periodicals as Topic; D009626:Terminology as Topic; D018875:Vocabulary, Controlled