Success in extracting biological relationships is mainly dependent on the 
complexity of the task as well as the availability of high-quality training 
data. Here, we describe the new corpora in the systems biology modeling language 
BEL for training and testing biological relationship extraction systems that we 
prepared for the BioCreative V BEL track. BEL was designed to capture 
relationships not only between proteins or chemicals, but also complex events 
such as biological processes or disease states. A BEL nanopub is the smallest 
unit of information and represents a biological relationship with its 
provenance. In BEL relationships (called BEL statements), the entities are 
normalized to defined namespaces mainly derived from public repositories, such 
as sequence databases, MeSH or publicly available ontologies. In the BEL 
nanopubs, the BEL statements are associated with citation information and 
supportive evidence such as a text excerpt. To enable the training of extraction 
tools, we prepared BEL resources and made them available to the community. We 
selected a subset of these resources focusing on a reduced set of namespaces, 
namely, human and mouse genes, ChEBI chemicals, MeSH diseases and GO biological 
processes, as well as relationship types 'increases' and 'decreases'. The 
published training corpus contains 11 000 BEL statements from over 6000 
supportive text excerpts. For method evaluation, we selected and re-annotated 
two smaller subcorpora containing 100 text excerpts. For this re-annotation, the 
inter-annotator agreement was measured by the BEL track evaluation environment 
and resulted in a maximal F-score of 91.18% for full statement agreement. In 
addition, for a set of 100 BEL statements, we do not only provide the gold 
standard expert annotations, but also text excerpts pre-selected by two 
automated systems. Those text excerpts were evaluated and manually annotated as 
true or false supportive in the course of the BioCreative V BEL track 
task.Database URL: http://wiki.openbel.org/display/BIOC/Datasets.