# Semantic similarity network

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A **semantic similarity network** (**SSN**) is a special form of [semantic network](/source/Semantic_network).[1] designed to represent concepts and their semantic similarity. Its main contribution is reducing the complexity of calculating semantic distances. Bendeck (2004, 2008) introduced the concept of *semantic similarity networks* (SSN) as the specialization of a semantic network to measure semantic similarity from ontological representations.[2] Implementations include genetic information handling.[3][4]

The concept is formally defined (Bendeck 2008) as a [directed graph](/source/Directed_graph), with concepts represented as [nodes](/source/Vertex_(graph_theory)) and semantic similarity relations as [edges](/source/Graph_(discrete_mathematics)).[5] The relationships are grouped into relation types. The concepts and relations contain attribute values to evaluate the [semantic similarity](/source/Semantic_similarity)[6] between concepts. The semantic similarity relationships of the SSN represent several of the general relationship types of the standard [Semantic network](/source/Semantic_network), reducing the complexity of the (normally, very large) network for calculations of semantics. SSNs define relation types as templates (and [taxonomy](/source/Taxonomy_(general)) of relations) for semantic similarity attributes that are common to relations of the same type. SSN representation allows propagation algorithms to faster calculate semantic similarities, including stop conditions within a specified threshold. This reduces the computation time and power required for calculation.

A more recent publications on Semantic Matching and Semantic Similarity Networks could be found in (Bendeck 2019).[7]

Specific Semantic Similarity Network application on healthcare was presented at the Healthcare information exchange Format (FHIR European Conference) 2019.[8][9]

The latest evolution in [Artificial Intelligence](/source/Artificial_Intelligence) (like [ChatGPT](/source/ChatGPT), based on [Large language model](/source/Large_language_model)), relay strongly on [evolutionary computation](/source/Evolutionary_computation), the next level will be to include [semantic unification](/source/Semantic_unification) (like in the [Semantic Networks](/source/Semantic_Networks) and this **Semantic similarity network**) to extend the current models with more powerful understanding tools.

## References

1. **[^](#cite_ref-1)** R. H. Richens: "General program for mechanical translation between any two languages via an algebraic interlingua". Cambridge Language Research Unit. *Mechanical Translation*, November 1956; p. 37

1. **[^](#cite_ref-2)** Fawsy Bendeck, Three Fold "Ontology + Model + Instance (OMI) – Semantic Unification Process, In International Conference on Advances in Internet, Processing, System and Interdisciplinary Research (IPSI-2004), Stockholm, Sep 2004, [ISBN](/source/ISBN_(identifier)) [86-7466-1173](https://en.wikipedia.org/wiki/Special:BookSources/86-7466-1173).

1. **[^](#cite_ref-3)** Jiang, R.; Gan, M.; He, P. (2011). ["Constructing a gene semantic similarity network for the inference of disease genes"](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3287482). *BMC Systems Biology*. **5** (2): 2. [doi](/source/Doi_(identifier)):[10.1186/1752-0509-5-S2-S2](https://doi.org/10.1186%2F1752-0509-5-S2-S2). [PMC](/source/PMC_(identifier)) [3287482](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3287482). [PMID](/source/PMID_(identifier)) [22784573](https://pubmed.ncbi.nlm.nih.gov/22784573).

1. **[^](#cite_ref-4)** Guzzi, P. H.; Veltri, P.; Cannataro, M. (2013). "Thresholding of semantic similarity networks using a spectral graph-based technique". *International Workshop on New Frontiers in Mining Complex Patterns*. Cham: Springer. pp. 201–213. [arXiv](/source/ArXiv_(identifier)):[1305.4858](https://arxiv.org/abs/1305.4858). [Bibcode](/source/Bibcode_(identifier)):[2013arXiv1305.4858H](https://ui.adsabs.harvard.edu/abs/2013arXiv1305.4858H).

1. **[^](#cite_ref-bendeck_5-0)** Bendeck, F. (2008). *WSM-P Workflow Semantic Matching Platform, PhD dissertation, University of Trier, Germany*. Verlag Dr. Hut. [ASIN](/source/Amazon_Standard_Identification_Number) [3899638549](https://www.amazon.de/dp/3899638549).

1. **[^](#cite_ref-6)** P. Resnik. Using Information Content to Evaluate Semantic Similarity in a Taxonomy. Proc. the 14th International Joint Conference on Artificial Intelligence, 448–453, 1995.

1. **[^](#cite_ref-bendeck2019_7-0)** Bendeck, Fawsy (2019). *Artificial Intelligence – Semantic Matching and Semantic Similarity Networks, Germany*. Verlag Dr. Hut. [ASIN](/source/Amazon_Standard_Identification_Number) [3843940762](https://www.amazon.de/dp/3843940762).

1. **[^](#cite_ref-8)** [FHIR Amsterdam](https://www.devdays.com/devdays-europe-2019/)

1. **[^](#cite_ref-9)** Recently reference in the (2017) [Deep_Semantic_Similarity_Neural_Network_(DSSNN)](http://www.gabormelli.com/RKB/Deep_Semantic_Similarity_Neural_Network_(DSSNN))

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Adapted from the Wikipedia article [Semantic similarity network](https://en.wikipedia.org/wiki/Semantic_similarity_network) by Wikipedia contributors ([contributor history](https://en.wikipedia.org/wiki/Semantic_similarity_network?action=history)). Available under [Creative Commons Attribution-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-sa/4.0/). Changes may have been made.