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Semantic similarity network

A semantic similarity network (SSN) is a special form of 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, with concepts represented as nodes and semantic similarity relations as edges.[5] The relationships are grouped into relation types. The concepts and relations contain attribute values to evaluate the semantic similarity[6] between concepts. The semantic similarity relationships of the SSN represent several of the general relationship types of the standard Semantic network, reducing the complexity of the (normally, very large) network for calculations of semantics. SSNs define relation types as templates (and taxonomy 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 (like ChatGPT, based on Large language model), relay strongly on evolutionary computation, the next level will be to include semantic unification (like in the Semantic Networks and this Semantic similarity network) to extend the current models with more powerful understanding tools.


References

  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
  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 86-7466-1173.
  3. ^ Jiang, R.; Gan, M.; He, P. (2011). "Constructing a gene semantic similarity network for the inference of disease genes". BMC Systems Biology. 5 (2): 2. doi:10.1186/1752-0509-5-S2-S2. PMC 3287482. PMID 22784573.
  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:1305.4858. Bibcode:2013arXiv1305.4858H.
  5. ^ Bendeck, F. (2008). WSM-P Workflow Semantic Matching Platform, PhD dissertation, University of Trier, Germany. Verlag Dr. Hut. ASIN 3899638549.
  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.
  7. ^ Bendeck, Fawsy (2019). Artificial Intelligence – Semantic Matching and Semantic Similarity Networks, Germany. Verlag Dr. Hut. ASIN 3843940762.
  8. ^ FHIR Amsterdam
  9. ^ Recently reference in the (2017) Deep_Semantic_Similarity_Neural_Network_(DSSNN)
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