BioKB - Text mining and semantic technologies for the biomedical content discovery

The ever-increasing number of publicly available biomedical articles calls for automatic information extraction from digitized publications. We have implemented a pipeline which, by exploiting text mining and semantic technologies, helps researchers easily access semantic content of thousands of abstracts and full text articles from PubMed and Elsevier. The text mining component analyzes the articles content and extracts relations between a wide variety of concepts, extending the scope from proteins, chemicals and pathologies to biological processes and molecular functions. Moreover, the relations are extracted along with the context which specifies localization of the detected events, preconditions, temporal and logic order, mutual dependency and/or exclusion. Extracted knowledge is stored in a knowledge base publicly available for both, human and machine access, via web interface and SPARQL endpoint. To address the data accessibility, reusability and interoperability, all the extracted relations are standardized using unique resource identifiers (URIs) and a custom ontology based on Genia ontology.