Semantic Net in AI

Knowledge Representation: 1960s Networks & Meaning

Ross Quillian (1966 and 1968) was among the early AI workers to develop a computational model which represented 'concepts' as hierarchical networks.

This model was amended with some additional psychological assumptions to characteristic the structure of [human] semantic memory.

Concepts can be represented as hierarchical of inter-connected concept nodes (e.g. animal, bird, canary)

Any concept has a number of associated attributes at a given level (e.g. animal --> has skin; eats etc.)

Some concept nodes are superordinates of other nodes (e.g. animal>bird) and some are subordinates (canary<bird)

For reason of cognitive economy, subordinates inherit all the attributes of their super ordinate concepts

Some instances of a concept are excepted from the attributes that help [human] to define the superordinates (e.g. ostrich is is excepted from flying)

Various [psychological] processes search these hierarchies for information about the concepts represented

Networks & Meaning

Inheritance
Specifies can be more detailed
Can be overridden --- cannot fly

Tests on human subjects showed that the subjects recognise propositions lower down the hierarchy (canary is a yellow bird) more readily than propositions higher up the hierarchy (canary has skin).

A semantic network is a structure for representing knowledge as a pattern of interconnected nodes and arcs. Nodes in the net represent concepts of entities , attributes, events , values. Arcs in the network represent relationships that hold between the concepts.

Concept labeled C111 and C112 inherit all the attributes of C11 which, in turn, inherits all the attributes of C1; similarly C121 inherits attributes of C12 and C12 of C1. All arcs are labeled is-a, which relates superordinates (C1) to subordinates (C11, C12) to instances (C111, C112, C121).