BACKGROUND: Information extraction (IE) efforts are widely acknowledged to be 
important in harnessing the rapid advance of biomedical knowledge, particularly 
in areas where important factual information is published in a diverse 
literature. Here we report on the design, implementation and several evaluations 
of OpenDMAP, an ontology-driven, integrated concept analysis system. It 
significantly advances the state of the art in information extraction by 
leveraging knowledge in ontological resources, integrating diverse text 
processing applications, and using an expanded pattern language that allows the 
mixing of syntactic and semantic elements and variable ordering.
RESULTS: OpenDMAP information extraction systems were produced for extracting 
protein transport assertions (transport), protein-protein interaction assertions 
(interaction) and assertions that a gene is expressed in a cell type 
(expression). Evaluations were performed on each system, resulting in F-scores 
ranging from .26-.72 (precision .39-.85, recall .16-.85). Additionally, each of 
these systems was run over all abstracts in MEDLINE, producing a total of 72,460 
transport instances, 265,795 interaction instances and 176,153 expression 
instances.
CONCLUSION: OpenDMAP advances the performance standards for extracting 
protein-protein interaction predications from the full texts of biomedical 
research articles. Furthermore, this level of performance appears to generalize 
to other information extraction tasks, including extracting information about 
predicates of more than two arguments. The output of the information extraction 
system is always constructed from elements of an ontology, ensuring that the 
knowledge representation is grounded with respect to a carefully constructed 
model of reality. The results of these efforts can be used to increase the 
efficiency of manual curation efforts and to provide additional features in 
systems that integrate multiple sources for information extraction. The open 
source OpenDMAP code library is freely available at 
http://bionlp.sourceforge.net/