Relying solely on full-text for searching is problematic when it comes to STM and scholarly information resources. That is because authors often use different terms for the same concept. Moreover, with a full-text search, highly relevant related concepts will not appear. For these reasons, SCM6 includes Silverchair’s semantic search engine which uses a full-text search in tandem with relevancy rankings based on a normalized concept vocabulary and the nearly 300,000 equivalents found in our Cortex Taxonomy. Silverchair creates a “semantic summary” for each unit of content, which is then used, in conjunction with a full-text search algorithm, to return more accurate relevancy rankings. For example, a user who searches for “STEMI” may receive results that include terms such as “ST-segment myocardial infarction,” “ST elevation MI,” “acute myocardial infarction,” “AMI,” and “heart attack” among others.