Semantic nets in Artificial Intelligence
Semantic nets in AI:
A semantic network is a graphic notation for representing knowledge in pattern interconnected nodes and area. In 1956, Richard H. R. of the Cambridge Language Research Unit (CLRU) developed Semantic networks for machine translation of natural languages. Semantic nets are used for a variety of functions, like knowledge representation.
Properties of Semantic Nets:
1. Expressiveness: Semantic nets allow the representation of facts and the relationship between facts. The level of the hierarchy provides a mechanism for representing general and specific knowledge. This representation is a model of human memory and it is therefore relatively understandable.
2. Effectiveness: It supports inference through property inheritance. It represented by PROLOG, LISP, and other AI languages making it amenable to computation.
3. Efficiency: It reduces the size of the knowledge base. knowledge stored only at its highest level of abstraction rather than for every instance or example of a class.
4. Explicitness: Reasoning equates to following paths through the network, so relationship and inference are explicit in the network lines.
Types of Semantic Networks in Artificial Intelligence:
There are mainly six types of semantic networks in Artificial Networks:
1. Definitional Networks: It emphasizes the subtype or is-a relationship between a concept type and a newly defined subtype.
2. Assertional Networks: It designed to assert propositions.
3. Implicational Networks: It uses implications as the primary relationship for connecting nodes.
4. Executable Networks: It contains mechanisms that can cause some change to the network itself.
5. Learning Networks: It builds or extends the representation by acquiring knowledge from examples.
6. Hybrid Networks: These combine two or more of the previous techniques, either in a single network or in separate, but closely interacting networks.