Semantic similarity or semantic relatedness is a metric defined over a set of documents or terms, where the idea of distance between them is based on the likeness of their meaning or semantic content as opposed to similarity which can be estimated regarding their syntactical representation (e.g. their string format). 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. 
Concretely, Semantic similarity can be estimated by defining a topological similarity, by using ontologies to define the distance between terms/concepts. For example, a naive metric for the comparison of concepts ordered in a partially ordered set and represented as nodes of a directed acyclic graph (e.g., a taxonomy), would be the shortest-path linking the two concept nodes. 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.
An extensive survey dedicated to the notion of semantic measures and semantic similarity is proposed in: Semantic Similarity from Natural Language and Ontology Analysis.
- 1 Taxonomy
- 2 Visualization
- 3 Applications
- 4 Measures
- 5 See also
- 6 References
- 7 External links
The concept of semantic similarity is more specific than semantic relatedness, as the latter includes concepts as antonymy and meronymy, while similarity does not . 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 is usually a number between -1 and 1, or between 0 and 1, where 1 signifies extremely high similarity.
An intuitive way of visualizing the semantic similarity of terms is by grouping together terms which are closely related and spacing wider apart the ones which are distantly related. This is also common in practice for mind maps and concept maps and is sometimes subconscious.
Semantic similarity measures have been applied and developed in biomedical ontologies, namely, the Gene Ontology (GO). They are mainly used to compare genes and proteins based on the similarity of their functions rather than on their sequence similarity, but they are also being extended to other bioentities, such as chemical compounds, anatomical entities and diseases.
These comparisons can be done using tools freely available on the web:
- ProteInOn can be used to find interacting proteins, find assigned GO terms and calculate the functional semantic similarity of UniProt proteins and to get the information content and calculate the functional semantic similarity of GO terms.
- CMPSim provides a functional similarity measure between chemical compounds and metabolic pathways using ChEBI based semantic similarity measures.
- CESSM provides a tool for the automated evaluation of GO-based semantic similarity measures.
Similarity is also applied to find similar geographic features or feature types:
- SIM-DL similarity server can be used to compute similarities between concepts stored in geographic feature type ontologies.
- Similarity Calculator can be used to compute how well related two geographic concepts are in the Geo-Net-PT ontology.
- The OSM Semantic Network can be used to compute the semantic similarity of tags in OpenStreetMap.
Natural Language Processing
Natural language processing (NLP) is a field of computer science related to the area of human–computer interaction. Sentiment analysis, Natural language understanding and Machine translation (Automatically translate text from one human language to another) are a few of the major areas where it is being used. For example, knowing one information resource in the internet, it is often of immediate interest to find similar resources. The Semantic Web provides semantic extensions to find similar data by content and not just by arbitrary descriptors.     
There are essentially two types of approaches that calculate topological similarity between ontological concepts:
- Edge-based: which use the edges and their types as the data source;
- Node-based: in which the main data sources are the nodes and their properties.
Other measures calculate the similarity between ontological instances:
- Pairwise: measure functional similarity between two instances by combining the semantic similarities of the concepts they represent
- Groupwise: calculate the similarity directly not combining the semantic similarities of the concepts they represent
- Pekar et al.
- Cheng and Cline
- Wu et al.
- Del Pozo et al.
- IntelliGO: Benabderrahmane et al.
- Resnik 
- based on the notion of information content. The information content of a concept (term or word) is the logarithm of the probability of finding the concept in a given corpus.
- only considers the information content of lowest common subsumer (lcs). A lowest common subsumer is a concept in a lexical taxonomy ( e.g. WordNet), which has the shortest distance from the two concepts compared. For example, animal and mammal both are the subsumers of cat and dog, but mammal is lower subsumer than animal for them.
- Lin 
- based on Resnik's similarity.
- considers the information content of lowest common subsumer (lcs) and the two compared concepts.
- Maguitman, Menczer, Roinestad and Vespignani 
- Generalizes Lin's similarity to arbitrary ontologies (graphs).
- Jiang and Conrath 
- based on Resnik's similarity.
- considers the information content of lowest common subsumer (lcs) and the two compared concepts to calculate the distance between the two concepts. The distance is later used in computing the similarity measure.
- DiShIn Disjunctive Shared Information between Ontology Concepts 
- Align, Disambiguate, and Walk: Random walks on Semantic Networks 
- maximum of the pairwise similarities
- composite average in which only the best-matching pairs are considered (best-match average)
- LSA (Latent semantic analysis) (+) vector-based, adds vectors to measure multi-word terms; (−) non-incremental vocabulary, long pre-processing times
- PMI (Pointwise mutual information) (+) large vocab, because it uses any search engine (like Google); (−) cannot measure relatedness between whole sentences or documents
- SOC-PMI (Second-order co-occurrence pointwise mutual information) (+) sort lists of important neighbor words from a large corpus; (−) cannot measure relatedness between whole sentences or documents
- GLSA (Generalized Latent Semantic Analysis) (+) vector-based, adds vectors to measure multi-word terms; (−) non-incremental vocabulary, long pre-processing times
- ICAN (Incremental Construction of an Associative Network) (+) incremental, network-based measure, good for spreading activation, accounts for second-order relatedness; (−) cannot measure relatedness between multi-word terms, long pre-processing times
- NGD (Normalized Google distance) (+) large vocab, because it uses any search engine (like Google); (−) can measure relatedness between whole sentences or documents but the larger the sentence or document the more ingenuity is required, Cilibrasi & Vitanyi (2007), reference below.
- NCD (Normalized Compression Distance)
- ESA (Explicit Semantic Analysis) based on Wikipedia and the ODP
- SSA (Salient Semantic Analysis) which indexes terms using salient concepts found in their immediate context.
- n° of Wikipedia (noW), inspired by the game Six Degrees of Wikipedia, is a distance metric based on the hierarchical structure of Wikipedia. A directed-acyclic graph is first constructed and later, Dijkstra's shortest path algorithm is employed to determine the noW value between two terms as the geodesic distance between the corresponding topics (i.e. nodes) in the graph.
- VGEM (Vector Generation of an Explicitly-defined Multidimensional Semantic Space) (+) incremental vocab, can compare multi-word terms (−) performance depends on choosing specific dimensions
- BLOSSOM (Best path Length On a Semantic Self-Organizing Map) (+) uses a Self Organizing Map to reduce high-dimensional spaces, can use different vector representations (VGEM or word-document matrix), provides 'concept path linking' from one word to another (−) highly experimental, requires nontrivial SOM calculation
- Good Common Subsumer-(GCS)-based Semantic Similarity Measure 
- Comment on application of semantics-based similarity to biomedical ontologies 
- Terminology extraction
- Coherence (linguistics)
- Semantic differential
- Semantic similarity network
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- List of related literature
- WordNet::Similarity (using WordNet as an ontology)
- WordNet Explorer (interactive graphic WordNet database editor)
- Survey articles:
- Semantic Measures, i.e., semantic similarity, distance, relatedness... (Harispe et al. 2015)
- Semantic Similarity Measures (C. d'Amato, S. Staab, N. Fanizzi, EKAW 2008, Springer-Verlag)
- Algorithm, Implementation and Application of the SIM-DL Similarity Server (Introduction to the SIM-DL Similarity Server)
- Semantic Measures Library (SML), a highly tunable and open source JAVA library dedicated to the computation and analysis of semantic measures. The library is generic as it can be used on multiple ontologies/terminologies e.g. Gene Ontology, Medical Subject Headings, SNOMED CT, WordNet, or semantic graphs expressed in RDF Schema, Web Ontology Language, Open Biomedical Ontologies languages. The core developers also maintain the SML-Toolkit, a set of command line tools giving non-developers access to SML functionalities e.g. to perform large scale computation of semantic measures. Tutorials, downloads and documentation for both the SML and the SML-Toolkit are available at http://www.semantic-measures-library.org/.
- WordNet-Similarity, an open source package for computing the similarity and relatedness of concepts found in WordNet
- UMLS-Similarity, an open source package for computing the similarity and relatedness of concepts found in the Unified Medical Language System (UMLS)
- SEMILAR - A Semantic Similarity Toolkit, java based library, tool, and data related to measuring similarity and relatedness of text in different granularity (all free for research purposes).
- ESA Semantic Relatedness A Web API to compute semantic relatedness between pairs of words or text excerpts
- Serelex Semantic Relatedness A Web service that finds semantically related words based on the Serelex semantic similarity measure, which relies on a text corpus and a set of lexico-syntactic patterns. Description of this service is available at Panchenko et al. (2013)
- Cosine Similarity computing service An online service that computes cosine text similarity between two documents
- WordNet-Similarity, a web interface to WordNet-Similarity
- UMLS-Similarity, a web interface to UMLS-Similarity
- Semantic Link, finds related words using Wikipedia-based mutual information (MI)
- UMBC SimService, a web interface to top N similar words and phrase similarity
- Rus, V., Lintean, M. C., Banjade, R., Niraula, N. B., & Stefanescu, D. (2013, August). SEMILAR: The Semantic Similarity Toolkit. In ACL (Conference System Demonstrations) (pp. 163-168).