There is an increasing interest in developing ontologies and controlled 
vocabularies to improve the efficiency and consistency of manual literature 
curation, to enable more formal biocuration workflow results and ultimately to 
improve analysis of biological data. Two ontologies that have been successfully 
used for this purpose are the Gene Ontology (GO) for annotating aspects of gene 
products and the Molecular Interaction ontology (PSI-MI) used by databases that 
archive protein-protein interactions. The examination of protein interactions 
has proven to be extremely promising for the understanding of cellular 
processes. Manual mapping of information from the biomedical literature to 
bio-ontology terms is one of the most challenging components in the curation 
pipeline. It requires that expert curators interpret the natural language 
descriptions contained in articles and infer their semantic equivalents in the 
ontology (controlled vocabulary). Since manual curation is a time-consuming 
process, there is strong motivation to implement text-mining techniques to 
automatically extract annotations from free text. A range of text mining 
strategies has been devised to assist in the automated extraction of biological 
data. These strategies either recognize technical terms used recurrently in the 
literature and propose them as candidates for inclusion in ontologies, or 
retrieve passages that serve as evidential support for annotating an ontology 
term, e.g. from the PSI-MI or GO controlled vocabularies. Here, we provide a 
general overview of current text-mining methods to automatically extract 
annotations of GO and PSI-MI ontology terms in the context of the BioCreative 
(Critical Assessment of Information Extraction Systems in Biology) challenge. 
Special emphasis is given to protein-protein interaction data and PSI-MI terms 
referring to interaction detection methods.