The biomedical research community stores research results on biological entities in large-scale databases for their experimental data analysis and, in addition, produces semantic resources such as ontologies for the annotation of the entities. For automatic literature analysis, we exploit these resources to improve information retrieval, information extraction, ontology development and the discovery of new knowledge. As a result, the text mining research team at the European Bioinformatics Institute has established state of the art solutions for the high-throughput analysis of the scientific literature leading to new bioinformatics services (Whatizit, UKPMC). Current efforts lead towards the standardisation of the scientific literature through standardised data resources, e.g. large-scale annotated corpora, state of the art terminological resources and a the validation of solutions against benchmarks and semantic resources.
The presentation will in addition focus on the benefits (and limitations) of the existing ontological resources and will describe several solutions, how the conceptual knowledge can be successfully exploited to filter out relevant information from the scientific literature. Altogether, literature analysis contributes to the semantic interoperability of biomedical data resources and is increasingly embedded into openly accessible Semantic Web IT solutions.
Presentation at SSR