UNIT V
Structured Representations of Knowledge
Structured Representations of Knowledge

A good system for the representation of complex structured
knowledge hould possess the following four properties.
Representational Adequacy: The ability to represent all kinds of
knowledge that are needed in the domain.

Inferential Adequacy: The ability to manipulate the
representational structures in such a way as to derive new
structures corresponding to new knowledge inferred from it.

Inferential Efficiency: The ability to incorporate into the
knowledge structure additional information.

Acquisitional Efficiency: The ability to acquire new information
easily.
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Structured Representations of Knowledge
Following are the two techniques for accomplishing these
objectives have been developed in A.I. system
Declarative method: (such as predicate) in which most of the
knowledge is represented as a static collection of facts
accompanied by a small set of general procedures for
manipulating them.
Advantages:
1. Each fact need only be stored once , regardless of the number
of different ways in which it can be used.
2. It is easy to add new facts to the system, without changing
either the other facts or the small procedures.
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Structured Representations of Knowledge
Procedural method: in which the bulk of the knowledge is
represented as procedures for using it.
Advantages:
1. It easy to represent knowledge of how to do things.
2. It easy to represent knowledge that does not fit well in to many
simple declarative schemes.
3. It is easy to represent heuristic knowledge of how to do things
efficiently.
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Some Common Knowledge Structure
ISA relationship: the relationships between objects in a hierachical
taxonomy. For example
DOG ISA PET
PET ISA ANIMAL
ANIMAL ISA LIVINGTHING
ISPART relationship: the relationships between objects that are
made up of a set of components, each of which is made up of a
set of components and so forth. For example
HAND ISPART OF BODY
FINGER ISPART OF HAND
FINGERNAIL ISPART OF FINGER
The above examples are shown in fig.(W6-5 & W6-6))
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Some Common Knowledge Structure
One of the most important properties of both ISA and ISPART
relations is transitivity. If
POODLE ISA DOG
DOG ISA PET
Then it must also be the case that
POODLE ISA PET
Consider the predicates
ISA(Marcus,man)
ISA(poodle,dog)
ISA(Marcus,Pompeian)
ISA(dog.pet)
ISA(Pompeian.Roman)
ISA(horse,livestock)
Then we add to the system the statement
∀x ∀y ∀z ISA(x,y) ∧ ISA(y,z) → ISA(x,z)
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Some Common Knowledge Structure
we can now easily prove that
ISA(Marcus,Roman)
ISA(poodle,pet)
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Semantic Nets :
Representing Knowledge: In a semantic net, information is
represented as a set of nodes connected with each other by a set
of labeled arcs , which represent relationships among nodes.
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Components of a Semantic Net



We can define a Semantic Net by specifying its fundamental
components:
Lexical part
nodes – denoting objects
links – denoting relations between objects
labels – denoting particular objects and relations
Structural part
the links and nodes form directed graphs
the labels are placed on the links and nodes
Semantic part
meanings are associated with the link and node labels
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Components of a Semantic Network

Procedural part
constructors allow creation of new links and nodes destructors
allow the deletion of links and nodes writers allow the creation
and alteration of labels readers can extract answers to
questions.
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Furniture
ISA
Person
Chair
ISA
Seat
ISA
OWNER
Me
ISPART
COLOR
My-Chair
COVERING
Leather
Tan
ISA
Brown
Semantic Nets : LISP Representation of a Semantic Net
ATOM
PROPERTY LIST
CHAIR
((ISA FURNITURE))
MY-CHAIR
((ISA CHAIR)
(COLOR TAN)
(COVERING LEATHER)
(OWNER ME))
ME
((ISA PERSON))
TAN
((ISA BROWN)
SEAT
((ISPART))
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Semantic Net:
Some of the arcs from Figure could be represented in logic as
ISA(chair,furniture)
ISA(me,person)
COVERING( my-chair,leather)
COVER(my-chair,tan)
These are the Two-place predicates.
Similarly
MAN(Marcus)
Can be written as
ISA(marcus,man)
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ASemantic Net for N-place Predicates
Game
Blue
Visiting
Team
Isa
G23
Score
17-3
Home
Team
Red
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A Semantic Net Representing a Sentence
Give
John
Agent
Isa
EV7
Beneficiary
Mary
Object
BK23
Isa
Book
“John gave book to Mary”
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Reasoning with the Knowledge
The Semantic nets are used to find the relationship among the objects
by spreading activation out from each of the node and seeing where
the activation met. This process is called as intersection search.
Chair
Color
Tan
‘My chair is Tan’
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Reasoning with the Knowledge
John
Height
72
‘John height is 72’
John
Height
Greater-than
H1
Jill
Height
H2
‘John is taller than Jill’
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Inheritance




Inheritance is one of the main kind of
reasoning done in semantic nets
The ISA (is a) relation is often used
to link a class and its superclass.
Some links (e.g. Ispart) are inherited
along ISA paths
The semantics of a semantic net can
be relatively informal or very formal
Animal
isa
Bird ISPart Wings
isa
Robin
isa
Rusty
isa
Red
Advantages of Semantic nets

Easy to visualize

Formal definitions of semantic networks have been developed.

Related knowledge is easily clustered.

Efficient in space requirements

Objects represented only once

Relationships handled by pointers
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Disadvantages of Semantic nets

Inheritance (particularly from multiple sources and when exceptions
in inheritance are wanted) can cause problems.

Facts placed inappropriately cause problems.
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Conceptual Graphs


Conceptual graphs are semantic nets representing the meaning of
(simple) sentences in natural language
Two types of nodes:
 Concept nodes; there are two types of concepts, individual
concepts and generic concepts
 Relation nodes(binary relations between concepts)
GO
JOHN
Who
NEW YORK
How Where
BUS
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Frames




Frames – semantic net with properties
A frame represents an entity as a set of slots (attributes) and
associated values
A frame can represent a specific entry, or a general concept
Frames are implicitly associated with one another because the value
of a slot can be another frame

3 components of a frame
•frame name
•attributes (slots)
•values (fillers: list of
values, range, string, etc.)
Book Frame
Slot  Filler
•Title
 AI. A modern Approach
•Author  Russell & Norvig
•Year
 2003
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Inheritance

Similar to Object-Oriented programming paradigm
Hotel Chair
Hotel Room
•what
 room
•where hotel
•contains
–hotel chair
–hotel phone
–hotel bed
•what  chair
•height 20-40cm
•legs
4
Hotel Phone
•what  phone
•billing  guest
Hotel Bed
•what
•size
•part
 bed
king
 mattress
Mattress
•price
 100$
Benefits of Frames

Makes programming easier by grouping related knowledge

Easily understood by non-developers

Expressive power

Easy to set up slots for new properties and relations

Easy to include default information and detect missing values
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What is Knowledge ?

Makes programming easier by grouping related knowledge

Easily understood by non-developers

Expressive power

Easy to set up slots for new properties and relations

Easy to include default information and detect missing values
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What is Knowledge ?
Knowledge
Knowledge
Information
Data
Signal
Knowledge = Facts＋Rules＋Control Strategy
+(sometimes ) Faiths
Taxonomy of Knowledge

Facts: declarative knowledge


likes(john, wine)
Rules: procedural knowledge


thief(john),
may_steal(X, Y) if thief(X) and likes(X, Y)
Control Strategy: meta, super knowledge



reasoning strategy
note form
search strategy
Attributes of Knowledge

Range ：Special ←→ General

Intend ：Expository ←→ Instructional

Certainty ：Certain ←→ Uncertain

Contain/Conflict ：←→Contain Conflict（in faith）
Examples of Semantic Net (2)

My car is tan and John’s car is green
I
owner
car1
color
is-a
tan
car
is-a
car2
owner
john
color
green
Semantic Networks as Knowledge
Representations
Using Semantic Networks for representing knowledge has
particular advantages:
1. They allow us to structure the knowledge to reflect the structure of
that part of the world which is being represented.
2. The semantics, i.e. real world meanings, are clearly identifiable.
3. There are very powerful representational possibilities as a result of
“is a” and “is a part of” inheritance hierarchies.
4. They can accommodate a hierarchy of default values (for example,
we can assume the height of an adult male to be 178cm, but if we
know he is a baseball player we should take it to be 195cm).
5. They can be used to represent events and natural language
sentences.
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The End
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