The correct answer is: True.
Semantic networks are a type of knowledge representation that can be used to represent the meaning of words and concepts. They are made up of nodes and edges, where nodes represent concepts and edges represent relationships between concepts.
There are two main ways to infer using semantic networks:
- Intersection search: This involves finding all the nodes that are connected to both the starting node and the target node.
- Inheritance search: This involves finding all the nodes that are connected to the starting node and that have the target node as a descendant.
For example, if we have a semantic network with the following nodes and edges:
- Node: Person
- Node: Animal
- Edge: is-a (Person, Animal)
- Node: Dog
- Edge: is-a (Dog, Animal)
We can infer that a person is an animal by performing an intersection search. This will find all the nodes that are connected to both the “Person” node and the “Animal” node, which in this case is just the “Person” node itself.
We can also infer that a dog is an animal by performing an inheritance search. This will find all the nodes that are connected to the “Person” node and that have the “Animal” node as a descendant, which in this case is the “Dog” node.