Automated nanopublications generation from biomedical literature

The continuous growth of unstructured information resulting from biomedical research is a trending challenge for the scientific community. In this way, novel methods for information management are emerging to improve knowledge distribution and access. The concept of nanopublications illustrates one of these recent strategies to implement machine-readable knowledge assertions. It tries to overcome inconsistency, ambiguity and redundancy of traditional publications. The purpose is that they are more suited than traditional papers to represent relationships that exist between research data, providing an efficient mechanism for knowledge exchange. Although the evident benefits of these RDF-based snippets, its applicability stills challenging due to the inexistence of extraction and publications methods. To solve that issue, we propose an automated workflow for nanopublications generation from biomedical literature. The proposed method consists of exploring an automated information extraction tool for relevant information detection from published documents and respective standardization of the mined information through semantic web recommendations, for further exploration.