Semantic similarity

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Semantic similarity 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] The term semantic similarity is often confused with semantic relatedness. Semantic relatedness includes any relation between two terms, while semantic similarity only includes "is a" relations.[2] For example, "car" is similar to "bus", but is only related to "road" and "driving".

Computationally, 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]

Terminology

The concept of semantic similarity is more specific than semantic relatedness, as the latter includes concepts as antonymy and meronymy, while similarity does not.[3] 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

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

Biomedical informatics

Semantic similarity measures have been applied and developed in biomedical ontologies,[4][5][6] namely, the Gene Ontology (GO).[7][8][9][10] 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,[11] anatomical entities[12] and diseases.[13]

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.[14]
  • CMPSim provides a functional similarity measure between chemical compounds and metabolic pathways using ChEBI based semantic similarity measures.[15]
  • CESSM provides a tool for the automated evaluation of GO-based semantic similarity measures.[16]

GeoInformatics

Similarity is also applied to find similar geographic features or feature types:[17]

  • SIM-DL similarity server[18] 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.[19][20]
  • The OSM Semantic Network can be used to compute the semantic similarity of tags in OpenStreetMap.[21]

Computational linguistics

Several metrics use WordNet: (+) humanly constructed; (−) humanly constructed (not automatically learned), cannot measure relatedness between multi-word term, non-incremental vocabulary.[3][22]

Natural language processing

Natural language processing (NLP) is a field of computer science and linguistics. 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.[23][24][25][26] [27] [28] [29] [30] [31]

Measures

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:

Edge-based

  • Pekar et al.[32]
  • Cheng and Cline[33]
  • Wu et al.[34]
  • Del Pozo et al.[35]
  • IntelliGO: Benabderrahmane et al.[6]

Node-based

  • Resnik [36]
    • 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 [37]
    • based on Resnik's similarity.
    • considers the information content of lowest common subsumer (lcs) and the two compared concepts.
  • Maguitman, Menczer, Roinestad and Vespignani [38]
    • Generalizes Lin's similarity to arbitrary ontologies (graphs).
  • Jiang and Conrath [39]
    • 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 [40]
    • other alternative: GraSM (Graph-based Similarity Measure) [41]
  • Align, Disambiguate, and Walk: Random walks on Semantic Networks [42]

Pairwise

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

Groupwise

Statistical similarity

Statistical similarity approaches can be learned from data, or predefined. Similarity learning can often outperform predefined similarity measures. Broadly speaking, these approaches build a statistical model of documents, and use it to estate similarity.

  • LSA (Latent semantic analysis) [44][45](+) 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.[46]
  • 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
  • NASARI:[47] Sparse vector representations constructed by applying the hypergeometric distribution over the Wikipedia corpus in combination with BabelNet taxonomy. Cross-lingual similarity is currently also possible thanks to the multilingual and unified extension.[48]

Semantics-based similarity

  • Good Common Subsumer-(GCS)-based Semantic Similarity Measure [49]
  • Comment on application of semantics-based similarity to biomedical ontologies [50]

Gold standards

Researchers have collected datasets with similarity judgements on pairs of words, which are used to evaluate the cognitive plausibility of computational measures. For a list of datasets, see http://www.cs.cmu.edu/~mfaruqui/suite.html.

See also

References

  1. ↑ 1.0 1.1 Lua error in package.lua at line 80: module 'strict' not found.
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  3. ↑ 3.0 3.1 Lua error in package.lua at line 80: module 'strict' not found.
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  5. ↑ Lua error in package.lua at line 80: module 'strict' not found.
  6. ↑ 6.0 6.1 Lua error in package.lua at line 80: module 'strict' not found.
  7. ↑ 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
  8. ↑ Pesquita, C., Faria, D., Falcão, A., Lord, P., & Couto, F. (2009). Semantic similarity in biomedical ontologies. PLoS Computational Biology, 5:e1000443
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  23. ↑ Similarity-based Learning Methods for the Semantic Web (C. d'Amato, PhD Thesis)
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  25. ↑ Raveendranathan, P. (2005). Identifying Sets of Related Words from the World Wide Web. Master of Science Thesis, University of Minnesota Duluth.
  26. ↑ Wubben, S. (2008). Using free link structure to calculate semantic relatedness. In ILK Research Group Technical Report Series, nr. 08-01, 2008.
  27. ↑ Juvina, I., van Oostendorp, H., Karbor, P., & Pauw, B. (2005). Towards modeling contextual information in web navigation. In B. G. Bara & L. Barsalou & M. Bucciarelli (Eds.), 27th Annual Meeting of the Cognitive Science Society, CogSci2005 (pp. 1078–1083). Austin, Tx: The Cognitive Science Society, Inc.
  28. ↑ Navigli, R., Lapata, M. (2007). Graph Connectivity Measures for Unsupervised Word Sense Disambiguation, Proc. of the 20th International Joint Conference on Artificial Intelligence (IJCAI 2007), Hyderabad, India, January 6-12th, 2007, pp. 1683–1688.
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  31. ↑ Turney, P. (2001). Mining the Web for Synonyms: PMI versus LSA on TOEFL. In L. De Raedt & P. Flach (Eds.), Proceedings of the Twelfth European Conference on Machine Learning (ECML-2001) (pp. 491–502). Freiburg, Germany.
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  37. ↑ 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
  38. ↑ Ana Gabriela Maguitman, Filippo Menczer, Heather Roinestad, Alessandro Vespignani: Algorithmic detection of semantic similarity. WWW 2005: 107-116
  39. ↑ J. J. Jiang and D. W. Conrath. Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy. In International Conference on Research on Computational Linguistics (ROCLING X), pages 9008+, September 1997
  40. ↑ Couto, F. & Silva, M. (2011), Disjunctive Shared Information between Ontology Concepts: application to Gene Ontology. Journal of Biomedical Semantics, 2:5
  41. ↑ Couto, F., Silva, M., & Coutinho, P. (2007). Measuring semantic similarity between Gene Ontology terms. Data and Knowledge Engineering, 61:137–152
  42. ↑ M. T. Pilehvar, D. Jurgens and R. Navigli. Align, Disambiguate and Walk: A Unified Approach for Measuring Semantic Similarity.. Proc. of the 51st Annual Meeting of the Association for Computational Linguistics (ACL 2013), Sofia, Bulgaria, August 4–9, 2013, pp. 1341-1351.
  43. ↑ 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
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  47. ↑ J. Camacho-Collados, M. T. Pilehvar, and R. Navigli. NASARI: a Novel Approach to a Semantically-Aware Representation of Items. In Proceedings of the North American Chapter of the Association of Computational Linguistics (NAACL 2015), Denver, USA, pp. 567-577, 2015
  48. ↑ J. Camacho-Collados, M. T. Pilehvar, and R. Navigli. A Unified Multilingual Semantic Representation of Concepts. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics (ACL 2015), Beijing, China, July 27–29, pp. 741-751, 2015
  49. ↑ C. d'Amato, S. Staab, and N. Fanizzi. On the influence of description logics ontologies on conceptual similarity. Knowledge Engineering: Practice and Patterns, pages 48-63, 2008 doi:10.1007/978-3-540-87696-0_7
  50. ↑ 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
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  • Dong, H., Hussain, F., & Chang, E. (2011). A Context-aware Semantic Similarity Model for Ontology Environments. Concurrency and Computation: Practice and Experience.23(5) pp. 505–524
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  • Ferreira, J. & Couto, F. (2010). Semantic similarity for automatic classification of chemical compounds. PLoS Computational Biolology 6(9): e1000937, 2010.
  • Gabrilovich, E. and Markovitch, S. (2007). Computing Semantic Relatedness using Wikipedia-based Explicit Semantic Analysis, Proceedings of The 20th International Joint Conference on Artificial Intelligence (IJCAI), Hyderabad, India, January 2007.
  • Lee, M. D., Pincombe, B., & Welsh, M. (2005). An empirical evaluation of models of text document similarity. In B. G. Bara & L. Barsalou & M. Bucciarelli (Eds.), 27th Annual Meeting of the Cognitive Science Society, CogSci2005 (pp. 1254–1259). Austin, Tx: The Cognitive Science Society, Inc.
  • Lemaire, B., & Denhiére, G. (2004). Incremental construction of an associative network from a corpus. In K. D. Forbus & D. Gentner & T. Regier (Eds.), 26th Annual Meeting of the Cognitive Science Society, CogSci2004. Hillsdale, NJ: Lawrence Erlbaum Publisher.
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  • Navigli, R., Lapata, M. (2010). "An Experimental Study of Graph Connectivity for Unsupervised Word Sense Disambiguation". IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 32(4), IEEE Press, 2010, pp. 678–692.
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  • Wong, W., Liu, W. & Bennamoun, M. (2008) Featureless Data Clustering. In: M. Song and Y. Wu; Handbook of Research on Text and Web Mining Technologies; IGI Global. [ISBN 978-1-59904-990-8] (the use of NGD and noW for term and URI clustering)

External links

Software

Web Services

  • ↑ 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).
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