Semantic similarity

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Semantic similarity 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.

According to some opinions the concept of semantic similarity is different from semantic relatedness because semantic relatedness includes concepts as antonymy and meronymy, while similarity doesn't. However, much of the literature uses these terms interchangeably, along with terms like semantic distance. In essence, semantic similarity, semantic distance, and semantic relatedness all mean, "How much does term A have to do with term B?"

The answer to this question, as given by the many automatic measures of semantic similarity/relatedness, is usually a number, usually between -1 and 1, or between 0 and 1, where 1 signifies extremely high similarity/relatedness, and 0 signifies little-to-none.

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 subconscious - 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 words (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 such as a vector space model to correlate words and textual contexts from a suitable text corpus (co-occurrence).