Medical Semantic Search
Semantic (read intelligent) search increases accuracy and relevancy by understanding the meaning/context/intent of a user query. Queries using semantic search not only find relevant text based on keywords, but also based on related concepts and contexts. A keyword in a query may have connotations and/or related concepts that may not necessarily be explicit in a document or text.
Our search is made ‘intelligent by the ‘machine-understanding,’ context and intent from user queries as well as the machine reading (text analysis, semantic annotation and classification) of your medical information resources. This includes the use of automatic query suggestion.
The resultant word-sense query recognition (and reduced ambiguation) makes for better search results and faster, more relevant knowledge dissemination from which users can realise actionable insights and make better informed decisions.
We can uniformly tag (annotate) and classify across ALL your data/information using standard medical terminologies - plus any specific taxonomy, ontology or rule sets that you have or want to develop.
Users can search in a number of languages*, retrieving information either in English or in the original document language.
The semantic (intelligent) search, sematic annotation and document classification are all supported by a continually growing medical knowledge base of over 1.2 billion RDF statements.
How to get started?
• Check out the further information on our Semantic Annotation, CloudMarket or Documents pages
• Alternatively make contact via our Contacts page