Semantic network

Features
Clique Component Cut Cycle Data structure Edge Loop Neighborhood Path Vertex Adjacency list / matrix Incidence list / matrix
Types
Bipartite Complete Directed Hyper Labeled Multi Random Weighted

Topology
Random graph Erdős–Rényi Barabási–Albert Bianconi–Barabási Fitness model Watts–Strogatz Exponential random (ERGM) Random geometric (RGG) Hyperbolic (HGN) Hierarchical Stochastic block Blockmodeling Maximum entropy Soft configuration LFR Benchmark
Dynamics
Boolean network agent based Epidemic/SIR

Category:Network theory
Category:Graph theory

Information mapping

Topics and fields
Data visualization Graphic communication Infographics Information design Knowledge visualization Mental model Morphological analysis Ontology (information science) Schema (psychology) Visual analytics Visual language
Node–link approaches
Argument map Cladistics Cognitive map Concept lattice Concept map Conceptual graph Decision tree Dendrogram Graph drawing Hyperbolic tree Hypertext Issue map Issue tree Layered graph drawing Mind map Object–role modeling Organizational chart Pathfinder network Radial tree Semantic network Sociogram Timeline Topic map Tree structure ZigZag
See also
Design rationale Diagrammatic reasoning Entity–relationship model Geovisualization List of concept- and mind-mapping software Olog Ontology (philosophy) Problem structuring methods Semantic Web Treemapping Wicked problem

A semantic network, or frame network is a knowledge base that represents semantic relations between concepts in a network. This is often used as a form of knowledge representation. It is a directed or undirected graph consisting of vertices, which represent concepts, and edges, which represent semantic relations between concepts, mapping or connecting semantic fields. A semantic network may be instantiated as, for example, a graph database or a concept map. Typical standardized semantic networks are expressed as semantic triples.

Semantic networks are used in natural language processing applications such as semantic parsing and word-sense disambiguation. Semantic networks can also be used as a method to analyze large texts and identify the main themes and topics (e.g., of social media posts), to reveal biases (e.g., in news coverage), or even to map an entire research field.