What are the semantic networks and How it is different from partitioned semantic networks?

Semantic Networks

Semantic nets (semantic networks) are used for the graphical representation of interrelationship between the elements in a knowledge domain, based on patterns of nodes interconnected by arcs. In semantic net, the knowledge is represented by labeled, directed graphs, whose nodes represent the domain elements and arcs, the relationship between the elements. The arcs, again are directed links and basically represent binary relations.

Originally, semantic nets were introduced for the representation of linguistic knowledge required for machine translation. Today they have found general applications in knowledge representation in various areas of AI.

Thus semantic net is an alternative to predicate logic as a form of knowledge representation. The idea is that we can store our knowledge in the form of a graph. A semantic net consists of nodes connected by arcs. Nodes represent objects in the real world. Arcs represent relationships between those objects. Inheritance is also possible in semantic nets. Inheritance is a process by which the local information of a superclass node is assumed by a class node, a subclass node, and an instance node. The semantic network-based knowledge representation mechanism is useful where an object or concept is associated with many attributes and where the relationship between objects is important.

How semantic net is different from the partitioned semantic net?

A partitioned semantic net is a semantic net which can be divided into one or more networks for the description of an individual network. The main idea of partitioning is to allow groups, nodes, and arcs to be bundled together into units called spaces, spaces are the fundamental entities in partitioned network on the same level as nodes and arcs. Every node and every arc of the network belongs to one or more spaces. Some spaces are used to encode background information or generic relations.