The ability to quantify the degree to which concepts are similar or related to each other is a key component in many Natural Language Processing (NLP) and Artificial Intelligence (AI) applications. For example, in a document search application, it can be very useful to identify text snippets that contain terms that are similar to (but not identical) to those provided by a user. This tutorial will introduce the theory behind measures of semantic similarity and relatedness, and show how these can be applied in the medical domain by using freely–available open–source software 1 (UMLS::Similarity). This software takes advantage of the Unified Medical Language System 2 (UMLS), which is maintained by the National Library of Medicine (USA). The tutorial will also show how to evaluate existing measures with manually–created reference standards
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