- Also referred to as Associative nets. A graphic notation for representing knowledge in patterns of interconnected nodes. The knowledge is represented in the form of graphical networks, consisting of nodes (representing objects) and arcs (describing the relationship between those objects). They are easy to understand and can be easily extended. They can categorize the object in different forms and can also link those objects. Mainly two types of relations are contained: IS-A relation (Inheritance) and Kind-of-relation.
- Main Components of SN: Lexical component: nodes denoting physical objects or links are relationships between objects; labels denote the specific objects and relationships; Structural component: the links or nodes from a diagram which is directed; Semantic component: Here the definitions are related only to the links and label of nodes, whereas facts depend on the approval areas; Procedural part: constructors permit the creation of the new links and nodes. The removal of links and nodes are permitted by destructors.
- Advantages and disadvantages of SN are given in the table below:
| Advantages | Disadvantages |
| More natural than the logical representationPermits using of effective inference algorithm (graphical algorithm)Simple and easily implemented and understoodCan be used as a typical connection application among various fields of knowledgePermits a simple approach to investigate the problem spaceGives an approach to make the branches of related componentsReverberates with the methods of the people process dataCharacterized by greater cognitive adequacy compared to logic-based formalismA greater expressiveness compared to logic | No standard definition for link namesNot intelligent, dependent on the creatorLinks are not alike in function or form, confusion in links that asserts relationships and structural linksUndistinguished nodes that represent classes and that represents individual objectsLinks on object represent only binary relationsNegation and disjunction and general taxonomical knowledge are not easily expressed |
- Mostly used types: Definitional Networks, Assertional Networks, Implication (Implicational) or Belief Networks, Executable Networks (including Petri Nets), Learning Networks, Hybrid Networks, etc.
Examples: Various Semantic Network types [semantic1]
Literature
- [semantic1] Sowa, J. F., Semantic Networks. Encyclopedia of Artificial Intelligence, Wiley, second edition, 1992. http://www.jfsowa.com/pubs/semnet.pdf