These are mathematical tools used to estimate the strength of the semantic relationship between units of language, concepts or instances, through a numerical description obtained according to the comparison of information supporting their meaning or describing their nature.
Based on text analyses, semantic relatedness between units of language (e.g., words, sentences) can also be estimated using statistical means such as a vector space model to correlate words and textual contexts from a suitable text corpus.
The former is based on the use of datasets designed by experts and composed of word pairs with semantic similarity / relatedness degree estimation.
The second way is based on the integration of the measures inside specific applications such as information retrieval, recommender systems, natural language processing, etc.
In this approach a linguistic item such as a term or a text can be represented by generating a pixel for each of its active semantic features in e.g. a 128 x 128 grid.
This allows for a direct visual comparison of the semantics of two items by comparing image representations of their respective feature sets.
For example, when comparing two ontologies describing conferences, the entities "Contribution" and "Paper" may have high semantic similarity since they share the same meaning.
Researchers have collected datasets with similarity judgements on pairs of words, which are used to evaluate the cognitive plausibility of computational measures.