An exercise in kidney factomics : From article titles to RDF knowledge base

Motivation: There are many existing resources that integrate data between databases; they do this either semantically by the use of RDF and triplestores (e.g. Bio2RDF), or with web links and ID mapping services (e.g. PICR, eUtils). Results declared in the literature are, however, only rarely interlinked with existing databases and even more rarely interlinked with each other. We describe a method to take factual statements reported in the literature and turn them into semantic networks of RDF triples. We use a method based on finding titles of papers that contain positive, direct statements about the outcome of a biomedical investigation. We then use dependency parsing and an ontological perspective to create and combine graphs of knowledge about a domain. Our aim in this work is to collect knowledge from the literature for inclusion in the Kidney and Urinary Pathway Knowledge Base (KUPKB), which will be used in the eLICO project to illustrate the utility of data-mining methods for biomarker discovery and pathway modelling.