Translational neuroscience in the field of spinal

cord injuries (SCI) faces a strong disproportion

between immense preclinical research efforts

and a lack of therapeutic approaches success-

ful in human patients: Currently, preclinical

research on SCI yields more than 3,000 new

publications per year (8,000 when including

the whole central nervous system, growing at

an exponential rate), whereas none of the result-

ing therapeutic concepts has led to functional

recovery of neural tissue in humans. Improving

clinical researchers’ information access there-

fore carries the potential to support more effec-

tive selection of promising therapy candidates

from preclinical studies. Thus, automated in-

formation extraction from scientific publica-

tions contributes to enabling meta studies and

therapy grading by aggregating relevant infor-

mation from the entire body of previous work

on SCI.

We present SCIE, an automated information

extraction pipeline capable of detecting rele-

vant information in SCI publications based on

ontological entity and probabilistic relation de-

tection. The input are plain text or PDF doc-

uments. As output, the user choses between

an online visualization or a machine-readable

format. Compared to human gold standard

annotations, our system achieves an average

extraction performance of 76 % precision and

52 % recall (F1-measure 0.59).

An instance of the webservice is available at

http://scie.sc.cit-ec.uni-bielefeld.de/. SCIE is

free software licensed under the AGPL and can

be downloaded for local installation at http:

//opensource.cit-ec.de/projects/scie/.