Semantic similarity

Semantic similarity

Semantic similarity or semantic relatedness 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.

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).



The concept of semantic similarity is more specific than semantic relatedness, as the latter includes concepts as antonymy and meronymy, while similarity does not .[1] 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/relatedness, and 0 signifies little-to-none.


An intuitive way of visualising the semantic similarity of terms is by grouping together closer related terms and spacing more distantly related ones wider apart. This is also common - if sometime subconscious - practice for mind maps and concept maps.


Biomedical Informatics

Semantic similarity measures have been applied and developed in biomedical ontologies,[2] [3]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[4] and diseases.[5]

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:


Several metrics use WordNet: (+) humanly constructed; (−) humanly constructed (not automatically learned), cannot measure relatedness between multi-word term, non-incremental vocabulary


Topological similarity

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

Some examples:



  • Resnik [6]
    • based on the notion of information content
  • Lin [7]
  • Jiang and Conrath [8]
  • DiShIn Disjunctive Shared Information between Ontology Concepts [9]
    • other alternative: GraSM (Graph-based Similarity Measure) [10]


  • maximum of the pairwise similarities
  • composite average in which only the best-matching pairs are considered (best-match average)


Statistical similarity

  • 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. [12]
  • ESA (Explicit Semantic Analysis) based on Wikipedia and the ODP
  • 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
  • SimRank


  • 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)

Web Services

See also


  1. ^ Budanitsky, Alexander; Hirst, Graeme (2001). "Semantic distance in WordNet: An experimental, application-oriented evaluation of five measures". Workshop on WordNet and Other Lexical Resources, Second meeting of the North American Chapter of the Association for Computational Linguistics. Pittsburgh 
  2. ^ Pesquita, Catia; Faria, Daniel; Falcão, André O.; Lord, Phillip; Couto, Francisco M. (2009). Bourne, Philip E.. ed. "Semantic Similarity in Biomedical Ontologies". PLoS Computational Biology 5 (7): e1000443. doi:10.1371/journal.pcbi.1000443. PMC 2712090. PMID 19649320. 
  3. ^ Benabderrahmane, Sidahmed; Smail Tabbone, Malika; Poch, Olivier; Napoli, Amedeo; Devignes, Marie-Domonique. (2010). "IntelliGO: a new vector-based semantic similarity measure including annotation origin". Biomed Central 11: 588. doi:10.1186/1471-2105-11-588. PMID 21122125. 
  4. ^ Ferreira, João D.; Couto, Francisco M. (2010). Mitchell, John B. O.. ed. "Semantic Similarity for Automatic Classification of Chemical Compounds". PLoS Computational Biology 6 (9): e1000937. doi:10.1371/journal.pcbi.1000937. PMC 2944781. PMID 20885779. 
  5. ^ Köhler, S; Schulz, MH; Krawitz, P; Bauer, S; Dolken, S; Ott, CE; Mundlos, C; Horn, D et al. (2009). "Clinical diagnostics in human genetics with semantic similarity searches in ontologies". American journal of human genetics 85 (4): 457–64. doi:10.1016/j.ajhg.2009.09.003. PMC 2756558. PMID 19800049. 
  6. ^ Philip Resnik. 1995. Using information content to evaluate semantic similarity in a taxonomy. In Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1 (IJCAI'95), Chris S. Mellish (Ed.), Vol. 1. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 448-453
  7. ^ Dekang Lin. 1998. An Information-Theoretic Definition of Similarity. In Proceedings of the Fifteenth International Conference on Machine Learning (ICML '98), Jude W. Shavlik (Ed.). Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 296-304
  8. ^ J. J. Jiang and D. W. Conrath. Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy. In International Conference Research on Computational Linguistics (ROCLING X), pages 9008+, September 1997
  9. ^ Couto, F. & Silva, M. (2011), Disjunctive Shared Information between Ontology Concepts: application to Gene Ontology. Journal of Biomedical Semantics, 2:5
  10. ^ Couto, F., Silva, M., & Coutinho, P. (2007). Measuring semantic similarity between Gene Ontology terms. Data and Knowledge Engineering, 61:137–152
  11. ^ Catia Pesquita, Daniel Faria, Hugo Bastos, António Ferreira, Andre O Falcao, Francisco Couto 2008: Metrics for GO based protein semantic similarity: a systematic evaluation. BMC Bioinformatics Suppl 5(9), S4
  12. ^ |title= Google Similarity Distance


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  • Couto, F., Silva, M., & Coutinho, P. (2003). Implementation of a functional semantic similarity measure between gene-products. DI/FCUL TR 03–29, University of Lisbon
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External links

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