Semantic similarity, variously also called 'semantic closeness/proximity/nearness', is a concept whereby a set of documents or terms within term lists are assigned a metric based on the likeness of their meaning / semantic content.

An intuitive way of displaying terms according to their semantic similarity is by grouping together closer related terms and spacing more distantly related ones wider apart. This is common - if sometime subconcious - practice for mind maps and concept maps.

Concretely, this can be achieved for instance by defining a topological similarity, by using ontologies to define a distance between word (a naive metric for terms arranged as nodes in a directed acyclic graph like a hierarchy would be the minimal distance (in separating edges) between the two term nodes), or using statistical means to correlate words and textual contexts.