AI Topic 3 Module Title

Module Title
Topic 3:
Knowledge Representation

© NCC Education Limited

Topic 3 Knowledge Representation - 3.2

Scope and Coverage

This topic will cover:

• Definition of knowledge
• Types of knowledge
• Logic representation
• Semantic network representation
• Frame representation
• Conceptual graph representation
• Production rules

Topic 3 Knowledge Representation - 3.3

Learning Outcomes

By the end of this topic students will be able to:

• Explain and identify different types of knowledge.
• Apply knowledge representation using the logical,
semantic network, frame, and production rules
techniques.

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Topic 3 Knowledge Representation - 3.4

What is Knowledge?

• An intelligent system needs knowledge about the

real world for making decisions and reasoning to
act efficiently.
• Knowledge is theoretical or practical WISDOM

understanding of a subject or a domain. KNOWLEDGE

• Knowledge is also the sum of what

INFORMATION
is currently known, and can refer to
data, facts, and information used to DATA

solve problems. The Knowledge Pyramid

Topic 3 Knowledge Representation - 3.5

Types of Knowledge
• Describes something.
Declarative Knowledge • Also referred to as conceptual, propositional or
descriptive knowledge

• Knowing how to do or achieve something

Procedural Knowledge
• Also known as imperative knowledge

• Describes relationships between concepts and grouping of

Structural Knowledge something
• Is basic knowledge to problem-solving

• Rules of thumb built of previous experiences and

Heuristic Knowledge approaches
• Is representing knowledge of some experts in a subject

Topic 3 Knowledge Representation - 3.6

Types of Knowledge
• Knowledge about the other types of knowledge
Meta-Knowledge • E.g. metarules, tagging, taxonomies, knowledge
management, etc.

• “That which precedes”

A Priori Knowledge
• Is independent of experience and universally true

• “That which follows”

A Posteriori Knowledge
• Derived from experience or empirical evidence

• Unconscious knowledge and cannot be expressed by

Tacit Knowledge language
• E.g. knowing how to walk, breath, etc.

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Topic 3 Knowledge Representation - 3.7

Knowledge Representation

• Knowledge representation (KS) is the study of

how to put knowledge into a form that a computer
can reason and manipulate with.
• The widespread growth of applications to real-
world problems caused an increase in the
demands for workable knowledge representation
schemes (KRS).
• The aim of KR is to enable computers to make
decisions from the knowledge they have.

Topic 3 Knowledge Representation - 3.8

Knowledge Representation Schemes

• KRS are frameworks designed to represent some

categories of knowledge in computers.
• KRS can be classified into 4 categories:

 Logic
• Use formal logic to represent knowledge.
• Examples: propositional and predicate calculus.

Topic 3 Knowledge Representation - 3.9

Knowledge Representation Schemes

 Procedural
• Represent knowledge with a set of sequential instructions to solve
problems
• Examples: flow chart, pseudocode, production rule, script.
 Network
• Represent knowledge as graph in which the nodes represent
object/concept in the problem domain and arcs represent
relations/associations between them.
• Examples: semantic network, state space, mind map.
 Structured
• Extend networks by allowing each node to be a complex data
structure consisting of property types and values.
• Example: frame.

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Topic 3 Knowledge Representation - 3.10

Knowledge Representation Schemes

(Poole & Mackworth, 2017)

The role of representations in solving problems

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Topic 3 Knowledge Representation - 3.11

Knowledge Representation Techniques

• A number of knowledge representation techniques

have been devised:
 Logic
 Semantic Network
 Frame
 Conceptual graph
 Rule

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Topic 3 Knowledge Representation - 3.12

Logic

• Logic is the study of making inferences – given a

set of facts, we attempt to reach a true conclusion.
• We use logic in our everyday lives – “should I buy
this car”, “should I seek medical attention”.
• People are not very good at reasoning because
they often fail to separate word meanings with the
reasoning process itself. Semantics refers to the
meanings we give to symbols.

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Topic 3 Knowledge Representation - 3.13

Logic

• An argument refers to the formal way facts and

rules of inferences are used to reach valid
conclusions. The process of reaching valid
conclusions is referred to as logical reasoning.
• Earliest form of logic was based on the syllogism –
developed by Aristotle.
• Example:
Premise: All cats are climbers.
Premise: Garfield is a cat.
Conclusion: Garfield is a climber.

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Topic 3 Knowledge Representation - 3.14

Logic

• Propositional Logic: The study of propositions, where

a proposition is a statement that is either true or false. It
is a method of manipulating symbols and sentences.
• A sentence represented symbolically is known as well-
formed formulae (wff), a syntactically correct formula .

Example:
If John is at the party, then Mary is too.
(p  q)
p = John is at the party, q = Mary is at the party

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Topic 3 Knowledge Representation - 3.15

Logic

• Predicate Logic: The study of predicates, where a

predicate is a characteristic or property that the subject
of a statement can have. Predicate logic is a richer
system than propositional logic, and it allows complex
facts about the world to be represented.

Example:
All elephants like peanuts. X elephant(X) like_peanut(X)
Everybody loves somebody. X Y love(X,Y)
Someone wrote the Deep Blue. X write(X, deep blue)

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Topic 3 Knowledge Representation - 3.16

Checkpoint Summary

• Knowledge is a theoretical or practical understanding

of a subject. Knowledge is the sum of what is
currently known.
• There are various types of knowledge, including
declarative knowledge, procedural knowledge, meta-
knowledge, heuristic knowledge and structural
knowledge.
• A large number of different representation schemes
and reasoning languages, including the use of logic,
semantic networks, frames, graphs and rules.

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Topic 3 Knowledge Representation - 3.17

Semantic Network

• Semantic network is a classic representation

technique for propositional information, which
introduced by Collins and Quillian (1969) to model
human memory.
• Propositions – a form of declarative knowledge,
stating facts (true/false), “atoms” that cannot be
further subdivided.
• Semantic networks consist of nodes (objects,
concepts, situations, facts) and arcs (relationships
between them).

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Topic 3 Knowledge Representation - 3.18

Semantic Network

• Example:
Common Types of
Relationships in
Semantic Networks:

IS-A – relates an
instance or individual to
a generic class

A-KIND-OF – relates
generic nodes to
generic nodes

Semantic network developed by Collins and Quillian (Luger, 2011)

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Topic 3 Knowledge Representation - 3.19

Semantic Network

• More examples:

(Giarratano & Riley, 2005)

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Topic 3 Knowledge Representation - 3.20

Semantic Network

• More examples:

(Luger, 2011)

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Topic 3 Knowledge Representation - 3.21

Frame

• Introduced by Marvin Minsky (1974). Frame

extends semantic network to provide a more
structured way of representing a knowledge base.
It stores properties, values, methods and relevant
information of object.
• Semantic nets provide 2-dimensional knowledge;
frames provide 3-dimensional.
• Frames represent related knowledge about narrow
subjects having much default knowledge.

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Topic 3 Knowledge Representation - 3.22

Frame

• A frame is a group of slots and fillers that defines

a stereotypical object that is used to represent
generic / specific knowledge.
• A slot stores filler (information) like specific value,
default value, inherited value, a pointer to
another frame (superclass or subclass).

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Topic 3 Knowledge Representation - 3.23

Frame

• Example: A Car Frame

(Giarratano & Riley, 2005)

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Topic 3 Knowledge Representation - 3.24

Frame

• Example:

(Luger, 2011)

Part of a frame description of a hotel room. “Specialization” indicates a pointer to a superclass.

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Topic 3 Knowledge Representation - 3.25

Conceptual Graph

• A conceptual graph (CG) is a finite, connected,

bipartite graph. The nodes of the graph are either
concepts (knowledge / fact / action) or conceptual
relations (type of relationship between 2 concepts).

• Rules of CG:
– NO arcs between a concept and another concept.
– NO arcs between a relation and another relation.
– All arcs either go from a concept to a relation or from a relation to a
concept.

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Topic 3 Knowledge Representation - 3.26

Conceptual Graph
• Examples:
A dog is brown A cat is on the mat
dog colour brown cat on mat

A monkey scratch its ear with a paw

part of

monkey agent scratch object ear

instrument

part of paw (Luger, 2011)

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Topic 3 Knowledge Representation - 3.27

Conceptual Graph

• Examples:
A cat is grey
General concept CG is referring to a
Cat: X colour grey
particular but unknown instance.

A cat named Tom is grey

Cat: Tom colour grey

Cat colour grey Specific concept CG is referring to a

particular and known instance.
name

“Tom”
(Luger, 2011)

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Topic 3 Knowledge Representation - 3.28

Conceptual Graph

“is a” “has a”

Garfield agent like object lasagna

“of” “which is” (Luger, 2011)

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Topic 3 Knowledge Representation - 3.29

Production Rule

• Rule is the most commonly used type of KR in AI,

can be defined as an IF-THEN structure that
relates given information or facts in the IF part to
some action in the THEN part.
• A rule provides some description of how to solve a
problem. Rules are relatively easy to create and
understand.

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Topic 3 Knowledge Representation - 3.30

Production Rule
• The IF part, called the antecedent (premise or condition)
and the THEN part called the consequent (conclusion or
action).
IF <antecedent>
THEN <consequent>

• A rule can have multiple antecedents joined by the

keywords AND (conjunction), OR (disjunction) or both.
IF <antecedent 1> IF <antecedent 1>
AND <antecedent 2> AND <antecedent 2>
……………………………. …………………………….
AND <antecedent n> OR <antecedent n>
THEN <consequent> THEN <consequent>

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Topic 3 Knowledge Representation - 3.31

Production Rule

• Example:
IF the ‘traffic light’ is green
THEN the action is go

IF the ‘traffic light’ is red

THEN the action is stop

IF ‘age of the customer’ < 18

AND ‘cash withdrawal’ > 1000
THEN ‘signature of the parent’ is required

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Topic 3 Knowledge Representation - 3.32

Summary

• We have discussed:
– The meaning of knowledge and knowledge
representation
– Different types of knowledge
– Some techniques of representing knowledge
• Fallacies may result from confusion between form of
knowledge and semantics.
• Different problems require different representation
techniques.

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Topic 3 Knowledge Representation - 3.33

References
• Russell, S., & Norvig, P. (2016). Artificial Intelligence: A
Modern Approach: Pearson
• Luger, G. F. (2011). Artificial Intelligence: Structures
and Strategies for Complex Problem Solving: Pearson
Education.
• Poole, D.L. and Mackworth, A.K. (2017). Artificial
Intelligence: foundations of computational agents:
Cambridge University Press.
• Giarratano, J. C., & Riley, G. (2005). Expert Systems:
Principles and Programming: Thomson Course
Technology.

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AI Topic 3 Module Title

Topic 3 – Knowledge Representation

Any Questions?

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