Page 75 - DCAP506_ARTIFICIAL_INTELLIGENCE

Unit 5: Knowledge Representation

A frame’s terminals are already filled with default values, which is based on how the human Notes
mind works.

Example: When a person is told “a boy kicks a ball,” most people will be able to visualize
a particular ball (such as a familiar soccer ball) rather than imagining some abstract ball with no
attributes.

5.2.2 Rules

If-then rules, which are arguably the most common form of knowledge representation in Artificial
Intelligence, are ambiguous. They can be interpreted both as logic programs having the form if
conditions then conclusions and as production rules having the form if conditions then do
actions. The relationship between these different kinds of rules has received little attention in
the AI literature; and, when it has, different authors have reached entirely different conclusions.
Some authors, such as Russell and Norvig in their textbook Introduction to Artificial Intelligence,
view production rules as just logical implications used to reason forward, while Herbert Simon
in the MIT Encyclopedia of Cognitive Science views the logic programming language Prolog as
one of many production system languages. On the other hand, Thagard in his Introduction to
Cognitive Science denies any relationship between logic and production rules at all.

5.2.3 Semantics

A semantic network is a network which represents semantic relations between the concepts.
This is often used as a form of knowledge representation. It is a directed or undirected graph
consisting of vertices, which represent concepts, and edges.

Example: An example of a semantic network is WordNet, a lexical database of English.
It groups English words into sets of synonyms called synsets, provides short, general definitions,
and records the various semantic relations between these synonym sets.
Some of the most common semantic relations defined are meronymy (A is part of B, i.e. B has A
as a part of itself), holonymy (B is part of A, i.e. A has B as a part of itself), hyponymy
(or troponymy) (A is subordinate of B; A is kind of B), hypernymy (A is superordinate of B),
synonymy (A denotes the same as B) and antonymy (A denotes the opposite of B). WordNet
properties have been studied from a network theory perspective and compared to other semantic
networks created from Roget’s Thesaurus and word association tasks respectively yielding the
three of them a small world structure.
It is also possible to represent logical descriptions using semantic networks such as the existential
Graphs of Charles Sanders Peirce or the related Conceptual Graphs of John F. Sowa. These have
expressive power equal to or exceeding standard first-order predicate logic. Unlike WordNet or
other lexical or browsing networks, semantic networks using these can be used for reliable
automated logical deduction. Some automated reasoners exploit the graph-theoretic features of
the networks during processing.

Self Assessment

Fill in the blanks:
4. ........................... is an area in artificial intelligence that is concerned with how to formally
“think”, that is, how to use a symbol system to represent “a domain of discourse”

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