  Integration of the scientific literature into a biomedical research
infrastructure requires the processing of the literature, identification of the
contained named entities (NEs) and concepts, and to represent the content in a
standardised way. The CALBC project partners (PPs) have produced a large-scale
annotated biomedical corpus with four different semantic groups through the
harmonisation of annotations from automatic text mining solutions (Silver
Standard Corpus, SSC). The four semantic groups were chemical entities and
drugs (CHED), genes and proteins (PRGE), diseases and disorders (DISO) and
species (SPE). The content of the SSC has been fully integrated into RDF Triple
Store (4,568,678 triples) and has been aligned with content from the GeneAtlas
(182,840 triples), UniProtKb (12,552,239 triples for human) and the lexical
resource LexEBI (BioLexicon). RDF Triple Store enables querying the scientific
literature and bioinformatics resources at the same time for evidence of
genetic causes, such as drug targets and disease involvement.
