Semantic similarity network
A semantic similarity network is a special form of semantic network. designed to represent concepts and their semantic similarity. Its main contribution is reducing the complexity of calculating semantic distances. Bendeck introduced the concept of semantic similarity networks as the specialization of a semantic network to measure semantic similarity from ontological representations. Implementations include genetic information handling.
The concept is formally defined as a directed graph, with concepts represented as nodes and semantic similarity relations as edges. The relationships are grouped into relation types. The concepts and relations contain attribute values to evaluate the semantic similarity 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 network for calculations of semantics. SSNs define relation types as templates 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.
Specific Semantic Similarity Network application on healthcare was presented at the Healthcare information exchange Format 2019.
The latest evolution in Artificial Intelligence, relay strongly on evolutionary computation, the next level will be to include semantic unification to extend the current models with more powerful understanding tools.