Abstract
Biologists have adopted ontologies: *To provide canonical representation of scientific knowledge *To annotate experimental data to enable interpretation, comparison, and discovery across databases *To facilitate knowledge-based applications for decisionsupport, natural language processing, and data integration But off-the-shelf solutions for the biologist to use ontologies are rare (versions, format, availability, license, overlap, etc.) Automatically process complex biological resources text content and generate annotations : * Large-scale - to scale up to many resources and ontologies * Automatic - to keep precision and accuracy * Easy to use and to access - web service approach * Customizable - to fit very specific needs * Smart - to leverage the knowledge contained in ontologies There have been success stories to reproduce: GO annotations, PubMed indexing, etc.