An algorithm for the prediction of annotations on Pubmed

The inference of novel knowledge and the generation of new hypotheses from the analysis of the current literature is a fundamental process in making new scientific discoveries. Especially in biomedicine, given the enormous amount of literature and knowledge bases available, this process is often complex, and researchers may focus too much on aspects already widely investigated due to poor literature mining. The automatic extraction of information in the form of semantically related terms (or tags) is becoming an aspect of great importance and extensive investigation (Kilicoglu et al., 2012; Stewart et al., 2012). Here we propose a method that consists of the combination of the TAGME algorithm (Ferragina and Scaiella, 2012), with the DT-Hybrid (Alaimo et al., 2013) technique for recommending novel semantically related tags. This combination will be designed in order to extract novel knowledge from a corpus of documents obtained from PubMed.