1. Knowledge Representation

2. CONTENTS What is knowledge? How to represent knowledge?
Definition of KR
Characteristics of KR Schemes
Types of KR Schemes:
Logic
Procedural
Network
Structured
Types of KR Methods:
Semantic Network
Frame
Script
Conceptual Graph
References

3. What is knowledge?
Knowledge is information/fact about some domain, subject area or about how to do something.
Knowledge can take many form. Some examples:
Eve is a female and Adam is a male.
All females with children are mothers.
Mothers are females, fathers are males.
2 x 5 = 10 and 5 x 2 = 10.
All students in UOFG are brainy.
Jerry is a mouse and Tom is a cat.
No mouse is bigger than a cat.

4. How to represent knowledge?
Can we use natural languages to represent knowledge?
Advantage:
Natural languages is
expressive enough.
Disadvantages:
Too ambiguous for
automated reasoning.
Semantic ambiguities:
Time flies like an arrow.
Pretty little girls’ school.
English teacher.
Syntactic ambiguities:
Bark in dog’s bark and tree bark.
Right in turn right and you’re right.
Bank in river bank and bank the money.
Time moves quickly just like an arrow does?
Measure the speed of a flying insects like you would measuring that of an arrow?
Measure the speed of flying insects like an arrow would?
Measure the speed of flying insects that are like arrows?
A type of flying insects i.e. “time-flies” that enjoy arrows?
Does the school look little?
Do the girls look little?
Does the school look pretty?
Do the girls look pretty?
January 2008 / Norshuhani Zamin
A teacher who teaches English?
A teacher from England?

5. How to represent knowledge?
Can we use databases to represent knowledge?
Yes, databases have been the most common option for software application to store and manipulate virtually any kind of data.
Advantage:
Well suited to efficiently representing and processing large amount of data.
Disadvantages:
Only simple problem domains can be accommodated.
Entities, relationship between entities can be represented but not much more.
Very simple reasoning i.e. simple lookup using SQL statements.
Very structured type records and hard to manipulate.

6. How to represent knowledge?
A record structure VS graph representation

7. Definition of KR
KR is the study on how information related to cognitive sciences can be appropriately encoded and utilized in computational models of cognition.
KR closely related with mental representation, deductive reasoning, philosophy of language and logic.
Also known as Knowledge Engineering.
KR has created a collection of formalized representational schemes and methods applied in most of the previous and current AI applications.
Creating appropriate knowledge representation schemes and methods for AI is not easy.

8. Characteristics of KR Schemes
KR scheme is the framework used to represent some categories of knowledge.
There are four characteristics of KR schemes:
Representational Adequacy
Ability to represent all necessary knowledge in a particular domain.
Inferential Adequacy
Ability to infer i.e. generate new knowledge from old knowledge.
The inferences made should be:
Sound: The new knowledge does follow from the old knowledge.
Complete: Making the right and logical inference.
Inferential Efficiency
Ability to combine new knowledge which can be used by inference mechanism in determining the best direction based on the current knowledge structure.
Acquisition Efficiency
Ability to add new information into knowledge base (by user or program)

9. Types of KR Schemes KR schemes can be classified into four categories:
Logic
Use formal logic to represent knowledge.
Examples: propositional and predicate calculus.
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.
An attempt to incorporate human memorizing ability into AI.
Examples: semantic network, state space, mind map (assignment).
Structured
Extend networks by allowing each node to be a complex data structure consisting of property types and values.
Example: frame.

10. Types of KR Methods: Semantic Network
Introduced by Quillian in 1968 as a model of human memory.
A knowledge representation in the form of graph.
The nodes of the graph correspond to facts.
The link of the arcs represent the relationships or associations between the facts.
Both nodes and arcs are labeled.
Semantic network supports inheritance.

11. Types of KR Methods: Semantic Network
Example:
Some possible inferences:
Bill is a cat that has 4 legs.
Bill is a mammal
Opus is a type of bird.
Opus is a penguin.
Opus cannot fly.

12. Types of KR Methods: Semantic Network4
More examples: 1 3
January 2008 / Norshuhani Zamin 2

13. Types of KR Methods: Semantic Network
Advantages:
Easy to visualize.
Relationship can be arbitrary defined by knowledge engineer.
Related knowledge easily clustered.
Efficient in space requirement.
Object defined only once (inheritance concept).
Disadvantages:
Unwanted inheritance may cause problems.
Inappropriately placed facts can cause problems.
No standard about node and arc values.

14. Types of KR Methods: Frame
Introduced by Marvin Minsky in 1974.
It extends semantic network to provide a more structured way of representing a knowledge base.
It stores properties, values, methods and relevant information of object.
Frame supports class hierarchies applied in object oriented concept.
Each frame has:
A name
A slot which stores information like specific value, default value, inherited value, a pointer to another frame (superclass or subclass)

15. Types of KR Methods: Frame
Example 1:

16. Types of KR Methods: Frame
Animals
Alive T
Example 2:
Flies F
isa
Birds
Mammals
Legs 2
Legs 4
Flies T
isa
Penguins
Cats
Bats
Flies F
Legs 2
Flies T
instance
Opus
Bill
Pat
Name
Opus
Name
Bill
Name
Pat
Friend
Friend

17. Types of KR Methods: Frame
Advantages:
Very flexible for many type of applications.
Similar to human knowledge organization.
Suitable for causal knowledge.
Easy to include default information and detect missing values.
Easier to understand than logic or rules.
Disadvantages:
No standard for slots filler values.
More on general representation than a specific representation.
For example, frame of a class room for primary and tertiary education should be different.
No associated reasoning or inference mechanism.

18. Types of KR Methods: Script
A structured representation describing a stereotyped sequence of events related to a particular context introduce by Roger Schank in 1977.
They are used in natural language understanding to understand specific situation.
Components of script:
Entry condition
conditions that must be true for the scripts to be valid.
Results
facts that become true once the script is completed.
Props
secondary things that support the script.
Roles
actions that individual participants perform.
Scenes
there are many scenes in a script. Each one represents a specific temporal aspect of the script.

19. Types of KR Methods: Script
For example, a script for the process..
“Going to a restaurant to have a meal”

20. Types of KR Methods: Conceptual Graph
Conceptual Graph (CG) is finite, connected bipartite graph or bigraph.
A bipartite graph is a graph in which the set of nodes can be partitioned in two disjoint sets.
There 2 types of node in the CG:
Concept: the knowledge / fact / action
Relation: the type of relationship between 2 concepts.
Rule in CG:
there are NO arcs between a concept and another concept, and 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.
CONCEPT A
RELATION
CONCEPT B
CONCEPT A
CONCEPT B
RELATION A
RELATION B
Valid CG
Invalid CG

21. Types of KR Methods: Conceptual Graph
Examples:
A dog is brown
A cat is on the mat
dog
color
brown
cat on
mat
A monkey scratch its ear with a paw
part of
monkey
scratch
agent
object
ear
instrument
paw
part of

22. Types of KR Methods: Conceptual Graph
General Concept VS Specific Concept
A cat is grey
General concept CG is referring to a particular but unknown instance.
Cat: X
color
grey
A cat named Abu is grey
Cat: Abu
color
grey
Cat
color
grey
Specific concept CG is referring to a particular and known instance.
name
“Abu”

23. Types of KR Methods: Conceptual Graph
Propositional Concept:
All people like pizza
Person: 
agent
like
object
pizza
Ali believes that all people likes pizza
Person: Ali
agent
believes
object
proposition:
Person: 
agent
like
object
pizza
There are no pink dogs
proposition:
negation
dog
color
pink

24. Types of KR Methods: Conceptual Graph
Joining Concept:
Doraemon eats dorayaki greedily
cat:Doraemon
person
agent
eat
object
pie
dorayaki
pie
pie
pie
pie
pie
cat:Doraemon
agent
eat
manner
pie
greedy
pie
pie
pie
pie
pie
object
pie
pie
dorayaki
pie
pie
pie
pie
cat:Doraemon
agent
eat
manner
greedy

25. References
Books:
1. Artificial Intelligence: Structures and Strategies for Complex Problem Solving
Author: George F Luger
Publisher: Addison Wesley
2. Textbook – Chapter 10
Websites:
Good thesis discussed how to represent CG in programming: