Currently, biomedical research critically depends on knowledge availability for flexible
re-analysis and integrative post-processing. The voluminous biological data already stored in
databases, put together with the abundant molecular data resulting from the rapid adoption of
high-throughput techniques, have shown the potential to generate new biomedical discovery
through integration with knowledge from the scientific literature.
Reliable information extraction applications have been a long-sought goal of the biomedical
text mining community. Both named entity recognition and conceptual analysis are needed in
order to map the objects and concepts represented by natural language texts into a rigorous
encoding, with direct links to online resources that explicitly expose those concepts semantics
(see Figure 1).P08-TIC-4299 of J. ASevilla and
TIN2009-13489 of DGICT, Madri
