Social and Semantic Web Technologies for the Text-To-Knowledge Translation Process in Biomedicine Cano Gutiérrez, Carlos Labarga, Alberto Blanco Morón, Armando Inteligencia artificial Artificial intelligence Currently, biomedical research critically depends on knowledge availability for flexible re-analysis and integrative post-processing. The voluminous biological data already stored in databases, put together with the abundant molecular data resulting from the rapid adoption of high-throughput techniques, have shown the potential to generate new biomedical discovery through integration with knowledge from the scientific literature. Reliable information extraction applications have been a long-sought goal of the biomedical text mining community. Both named entity recognition and conceptual analysis are needed in order to map the objects and concepts represented by natural language texts into a rigorous encoding, with direct links to online resources that explicitly expose those concepts semantics (see Figure 1). 2022-11-11T13:08:19Z 2022-11-11T13:08:19Z 2011 book part Carlos Cano... [et al.] (2011). Social and Semantic Web Technologies for the Text-to-Knowledge Translation Process in Biomedicine, Biomedical Engineering, Trends, Research and Technologies, Dr. Sylwia Olsztynska (Ed.), ISBN: 978-953-307-514-3, InTech, Available from: http://www.intechopen.com/books/biomedical-engineering-trends-research-and-technologies/social-andsemantic- web-technologies-for-the-text-to-knowledge-translation-process-in-biomedicine 978-953-307-514-3 https://hdl.handle.net/10481/77933 eng http://creativecommons.org/licenses/by-nc-sa/4.0/ open access Atribución-NoComercial-CompartirIgual 4.0 Internacional Intech