Semantic Processing for Biomedical Research
Semantic MEDLINE integrates information retrieval, advanced natural language processing, automatic summarization, and visualization into a single Web portal. The application is intended to help manage the results of PubMed searches by condensing core semantic content in the citations retrieved. Output is presented as a connected interactive graph of semantic relations, with links to the original MEDLINE citations.
The ability to manipulate salient information across documents helps users keep up with the research literature and discover connections which might otherwise go unnoticed. Such an ability can have an impact on biomedicine by supporting scientific research. Researchers can use Semantic MEDLINE to implement the literature-based discovery methodology for hypothesis generation; in addition, they can use the discovery browsing paradigm to elucidate poorly understood biomedical topics.
Thomas Rindflesch has a Ph.D. in linguistics from the University of Minnesota and conducts research in natural language processing at the National Library of Medicine. He is developing Semantic MEDLINE, a biomedical information management application that combines document retrieval, semantic processing, and knowledge visualization to facilitate scientific discovery.