Many automated knowledge extraction and processing applications rely on computing semantic relatedness between textual labels representing biological and medical concepts. Because of the large amount of new findings, most methods benefit from making use of highly specific resources. 7th Space Interactive brought this information to us in their article, “Calculating semantic relatedness for biomedical use in a knowledge-poor environment.”
One solution being proposed is to use only a relatively generic and small document corpus and its statistics, without referring to a previously defined knowledge base; thus it does not assume a ‘closed’ problem. With this proposal, computation for two input texts is based on the idea of comparing the vocabulary associated with the best-fit documents related to those texts.
Semantic technology continues to grow and expand its uses. Search is just one of those. Access Innovations, developer of the M.A.I. machine-assisted indexing system, specializes in complex coding, tagging, and indexing.
Melody K. Smith
Sponsored by Access Innovations, the world leader in thesaurus, ontology, and taxonomy creation and metadata application.
