Semantic Similarity Neural Network (SSNN)

From GM-RKB
(Redirected from Semantic Similarity Network)
Jump to navigation Jump to search

A Semantic Similarity Neural Network (SSNN) is an Artificial Neural Network that is an edge-weighted graph where the nodes are concepts and each edge has an associated weight that represents paired nodes semantic similarity.



References

2021

  • (Wikipedia, 2021) ⇒ https://en.wikipedia.org/wiki/Semantic_similarity_network Retrieved:2021-7-30.
    • 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. The concept is formally defined (Bendeck 2008) as a directed graph, with concepts represented as nodes and semantic similarity relations as edges.[3] The relationships are grouped into relation types. The concepts and relations contain attribute values to evaluate the semantic similarity [4] 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.
  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, .
  3. Bendeck, F. (2008). WSM-P Workflow Semantic Matching Platform, PhD dissertation, University of Trier, Germany. Verlag Dr. Hut. ASIN 3899638549
  4. P. Resnik. Using Information Content to Evaluate Semantic Similarity in a Taxonomy. Proc. the 14th International Joint Conference on Artificial Intelligence, 448–453, 1995.

2017

2017 AutomaticQuestionAnsweringUsing Fig4.png
Figure 4: The block-diagram of the proposed similarity network.

2016

2016 CapturingSemanticSimilarityforE Fig1.png
Figure 1: Extraction of convolutional vector space features $f_C\left (x, t_e\right)$. Three types of information from the input document and two types of information from the proposed title are fed through convolutional networks to produce vectors, which are systematically compared with cosine similarity to derive real-valued semantic similarity features.

2013

2011

2011 ConstructingaGeneSemanticSimila Fig1.png
Figure 1: Illustration of the procedure for constructing a gene semantic similarity network.