Automatic extraction of biological network information is one of the most 
desired and most complex tasks in biological and medical text mining. Track 4 at 
BioCreative V attempts to approach this complexity using fragments of 
large-scale manually curated biological networks, represented in Biological 
Expression Language (BEL), as training and test data. BEL is an advanced 
knowledge representation format which has been designed to be both human 
readable and machine processable. The specific goal of track 4 was to evaluate 
text mining systems capable of automatically constructing BEL statements from 
given evidence text, and of retrieving evidence text for given BEL statements. 
Given the complexity of the task, we designed an evaluation methodology which 
gives credit to partially correct statements. We identified various levels of 
information expressed by BEL statements, such as entities, functions, relations, 
and introduced an evaluation framework which rewards systems capable of 
delivering useful BEL fragments at each of these levels. The aim of this 
evaluation method is to help identify the characteristics of the systems which, 
if combined, would be most useful for achieving the overall goal of 
automatically constructing causal biological networks from text.