Recent disease networks studies have demonstrated that the integration of biological knowledge from several sources could lead to biomedical discovery. In this context, it is particularly attractive to connect the dots using the wealth of knowledge in the published literature. Biomedical literature contains high quality and high-confidence information on genes that have been studied for decades, including the gene’s relevance to a disease, its reaction mechanisms, structural information and well characterized interactions. However, an accurate and normalized representation of facts and the mapping of the information contained within papers with existing databases, ontologies and online resources has traditionally been almost negligible.

Recent developments in text mining and web technologies are being used for semantic enhancement of scholarly journals articles, providing better linking to other resources, adding descriptive metadata that assist article discovery and specify the meaning of concepts and terms within the article.

The research reported here aims to investigate how to annotate atomic components of research papers in life sciences by combining ontology-based and user-generated tags within a social network built upon these tagged concepts. The underlying interest of the authors is to move closer from the collected intelligence to the collective intelligence within life sciences. In this paper we would like to present a web-based open source resource for supporting distributed collaborative annotation efforts. Key assertions are extracted from literature based on a user query, automatically annotated and mapped to external resources. These annotated extracts can be further corrected and enhanced through user manual intervention via a simple web interface. The results are then converted into Semantic / Linked Data format and can be made accessible via a SPARQL endpoint and a faceted search interface.  Additionally, benchmark corpora generated with BioNotate can be used for the evaluation and development of automated relation extraction methods for biomedical literature mining.