Semantic similarity

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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.[1]

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 Measures for the Comparison of Units of Language, Concepts or Entities from Text and Knowledge Base Analysis.[1]

Taxonomy[edit]

The concept of semantic similarity is more specific than semantic relatedness, as the latter includes concepts as antonymy and meronymy, while similarity does not .[2] 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.

Visualization[edit]

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.

Applications[edit]

Biomedical Informatics[edit]

Semantic similarity measures have been applied and developed in biomedical ontologies,[3][4][5] namely, the Gene Ontology (GO).[6][7][8][9] 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,[10] anatomical entities[11] and diseases.[12]

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.[13]
  • CMPSim provides a functional similarity measure between chemical compounds and metabolic pathways using ChEBI based semantic similarity measures.[14]
  • CESSM provides a tool for the automated evaluation of GO-based semantic similarity measures.[15]

GeoInformatics[edit]

Similarity is also applied to find similar geographic features or feature types:[16]

  • SIM-DL similarity server[17] 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.[18][19]
  • The OSM Semantic Network can be used to compute the semantic similarity of tags in OpenStreetMap.[20]

Linguistics[edit]

Several metrics use WordNet: (+) humanly constructed; (−) humanly constructed (not automatically learned), cannot measure relatedness between multi-word term, non-incremental vocabulary [2][21]

Natural Language Processing[edit]

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.[22][23][24][25] [26] [27] [28] [29] [30]

Measures[edit]

Topological similarity[edit]

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:

Edge-based[edit]

  • Pekar et al.[31]
  • Cheng and Cline[32]
  • Wu et al.[33]
  • Del Pozo et al.[34]
  • IntelliGO: Benabderrahmane et al.[5]

Node-based[edit]

  • Resnik [35]
    • 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 [36]
    • based on Resnik's similarity.
    • considers the information content of lowest common subsumer (lcs) and the two compared concepts.
  • Maguitman, Menczer, Roinestad and Vespignani [37]
    • Generalizes Lin's similarity to arbitrary ontologies (graphs).
  • Jiang and Conrath [38]
    • 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 [39]
    • other alternative: GraSM (Graph-based Similarity Measure) [40]

Pairwise[edit]

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

Groupwise[edit]

Statistical similarity[edit]

  • LSA (Latent semantic analysis) [42][43](+) 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.[44]
  • 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
  • SimRank

Semantics-based similarity[edit]

  • Good Common Subsumer-(GCS)-based Semantic Similarity Measure [45]
  • Comment on application of semantics-based similarity to biomedical ontologies [46]

See also[edit]

References[edit]

  1. ^ a b Harispe S., Ranwez S. Janaqi S., Montmain J. (2013). "Semantic Measures for the Comparison of Units of Language, Concepts or Entities from Text and Knowledge Base Analysis". Arxiv Corr. 
  2. ^ a b 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 
  3. ^ Pesquita, Catia; Faria, Daniel; Falcão, André O.; Lord, Phillip; Couto, Francisco M. (2009). "Semantic Similarity in Biomedical Ontologies". In Bourne, Philip E. PLoS Computational Biology 5 (7): e1000443. doi:10.1371/journal.pcbi.1000443. PMC 2712090. PMID 19649320. 
  4. ^ Guzzi, Pietro Hiram; Mina, Marco; Cannataro, Mario; Guerra, Concettina (2012). "Semantic similarity analysis of protein data: assessment with biological features and issues". Briefings in Bioinformatics 13 (5): 569–585. doi:10.1093/bib/bbr066. PMID 22138322. 
  5. ^ a b 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. PMC 3098105. PMID 21122125. 
  6. ^ 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
  7. ^ Pesquita, C., Faria, D., Falcão, A., Lord, P., & Couto, F. (2009). Semantic similarity in biomedical ontologies. PLoS Computational Biology, 5:e1000443
  8. ^ Couto, F., Silva, M., & Coutinho, P. (2005). "Semantic similarity over the gene ontology: Family correlation and selecting disjunctive ancestors". Proc. of the ACM Conference in Information and Knowledge Management (CIKM): 343. doi:10.1145/1099554.1099658. ISBN 1595931406. 
  9. ^ Couto, F., Silva, M., & Coutinho, P. (2007). "Measuring semantic similarity between Gene Ontology terms". Data and Knowledge Engineering 61: 137–152. doi:10.1016/j.datak.2006.05.003. 
  10. ^ Ferreira, João D.; Couto, Francisco M. (2010). "Semantic Similarity for Automatic Classification of Chemical Compounds". In Mitchell, John B. O. PLoS Computational Biology 6 (9): e1000937. doi:10.1371/journal.pcbi.1000937. PMC 2944781. PMID 20885779. 
  11. ^ Ferreira, João D.; Couto, Francisco M. (2011). "Generic semantic relatedness measure for biomedical ontologies". ICBO 2011 Proceedings. 
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  14. ^ "CMPSim". 
  15. ^ "CESSM". 
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  17. ^ SIM-DL similarity server. CiteSeerX: 10.1.1.172.5544. 
  18. ^ "Geo-Net-PT Similarity Calculator". 
  19. ^ "Geo-Net-PT". 
  20. ^ "OSM Semantic Network". 
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  22. ^ Similarity-based Learning Methods for the Semantic Web (C. d'Amato, PhD Thesis)
  23. ^ Gracia, J. and Mena, E. (2008). "Web-Based Measure of Semantic Relatedness". Proceedings of the 9th international conference on Web Information Systems Engineering (WISE '08) (Springer-Verlag, Berlin, Heidelberg): 136–150. 
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  33. ^ Wu, H; Su, Z; Mao, F; Olman, V; Xu, Y (2005). "Prediction of functional modules based on comparative genome analysis and Gene Ontology application". Nucleic Acids Research 33 (9): 2822–37. doi:10.1093/nar/gki573. PMC 1130488. PMID 15901854. 
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  36. ^ 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
  37. ^ Ana Gabriela Maguitman, Filippo Menczer, Heather Roinestad, Alessandro Vespignani: Algorithmic detection of semantic similarity. WWW 2005: 107-116
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  39. ^ Couto, F. & Silva, M. (2011), Disjunctive Shared Information between Ontology Concepts: application to Gene Ontology. Journal of Biomedical Semantics, 2:5
  40. ^ Couto, F., Silva, M., & Coutinho, P. (2007). Measuring semantic similarity between Gene Ontology terms. Data and Knowledge Engineering, 61:137–152
  41. ^ 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
  42. ^ Landauer, T. K.; Dumais, S. T. (1997). "A solution to Plato's problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge". Psychological Review 104 (2): 211–240. doi:10.1037/0033-295x.104.2.211. 
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  46. ^ F. Couto and H. Pinto, The next generation of similarity measures that fully explore the semantics in biomedical ontologies, Journal of Bioinformatics and Computational Biology, vol. in press, 2013. preprint

External links[edit]

Software[edit]

[1]

Web Services[edit]

  • ^ 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).