Semantic Nets

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Semantic Net

Delhi College Of Engineering

HISTORY


“e a ti Nets
e e fi st i e ted fo o pute s Ri ha d H. Ri he
of the Cambridge Language Research Unit in 1956 as a i te li gua fo
machine translation of natural language. They were developed by Robert
.F.Simmons as System Development Corporation, California in the early
1960s.Later improved in the work of M.Ross Quilian in 1966 to use a sa
modal of human mind.
• Computer implementations of semantic networks were first developed for
artificial intelligence and machine translation, but before using in
computers and machine language it have long been used in philosophy
and psychology.

Semantic Net
• Human memory is a network of associations between
different pieces of knowledge. Thus we need a satisfactory
knowledge representation language which reflect the high
degree of interconnectivity between the pieces of information
contained in the human memory.
• Semantic net is one of the important way of graph based
representation of knowledge.
• In the semantic net the information is represented as a set of
nodes connected to each other by a set of labeled arcs, which
represent the relationship among them.
• This network contains examples of both the isa and instance
relations

Semantic Net(contd.)
• Nodes represent the objects and descriptive information
about those objects.
• Links describe the relationships between the nodes
• Object can be any physical item such as book, car, desk, or
even a person.
• Objects can also be concepts, events, or actions.
• Attributes of an object can also be used as nodes. These may
represent size, color, class, age etc.

Knowledge Representation Schemes: A
knowledge representation Scheme should have both procedural and
declarative schemes for effective organization of the knowledge base.
These are tools for knowledge representation:
1. Semantic Nets
2. Frames
3. Conceptual Dependency
4. Scripts
Semantic Networks: Definition: A Semantic Net is a structure for
representing knowledge as a pattern of inter connected nodes and
or
arcs.
It is defined as a graphical representation of knowledge in which the
nodes ( Objects under consideration) in a graph represent concepts
and the arcs represent binary relationships between concepts.

Following are the rules for nodes in most of semantic nets:1. Nodes in a Semantic net represent either:
a) Entities
b) Attributes
c) States or
d) Events

2. Arcs in a semantic net gives the relationship between the
nodes and labels on the arc specify what type of
relationship actually exists.
Example of a simple semantic net: We can add more knowledge
by linking other objects with different relationships.
Is-a
Scooter

Two-wheeler

Is-a

Motor-bike

Is-a

Brakes

has

Moving-vehicle
has

has
Engine

has

Electrical-system

Fuel-system

Fig. A Sample Semantic Net

From the above Semantic Net it is possible for us to say
that :
1. A scooter is a two wheeler and it is a moving vehicle.
2. A moving vehicle needs an engine (could be petrol or
diesel or any engine), a fuel engine system to sustain
the engine running, an electrical system for its lights ,
horns and also for initial ignition ( in case of petrol
vehicles ) and brakes ( of course, very important).
 Such semantic net not only gives details about an
object under consideration but also provides facilities
to represent variables. For example consider the
semantic net shown below:

Line -printer

has-a

Delhi University
Mini Computer
has-a
Computer
System
Centre

Is-a

HCL
Horizon III

has-a

has-part
Speed

Dumb-terminal
Part-of
Hammer-bank

X

has-part
Key Board

Delhi

Is-in

Delhi University

has-departments

Y

Fig: Representation of variables in Semantic Nets

has-part
Monitor

1. This semantic net has two variables X and Y as a part of the node.
2. This implies that the speed of the line printer could be 300 or 600
lines per minute.
3. Delhi university could have 18 or 25 or 30 departments.
Classification of Nodes in a Semantic Net:
Generally, the nodes in the semantic net are classified as:
1. Generic Nodes: It is a very general node. In the semantic network
on previous slide, for the semantic network of Delhi University centre,
the mini computer system is a generic node because many mini
computer systems exist and that node has to cater to all of them.
2. Individual or Instance nodes : Individual or instance nodes explicitly
state that they are specific instances of a generic node. HCL’s
Horizon III is an individual node because it is a very specific instance
of the mini-computer system.
Many link structures are being used today in semantic nets , some of
them are:
1. is_a
2. has_a 3. has_dept 4. contains 5. part_of
6. is_in etc.

Examples :- Both are semantic networks in below figure
1. Generic Node:Is_a

Two -wheeler

Moving-vehicle

2. Individual Node:Is_a

HCL Horizon III

Mini-Computer
System

Major feature if is_a link is that it generates hierarchal
structure within the network.
Is_a link has another major property which is called
inheritance . The property of inheritance is that the
properties which a most a generic node possesses are
transmitted to various specific instances of a generic node.
This property is called transitive property of inheritance.
 An individual or instance node forms a sub set of another
generic node etc.

Consider the Semantic net on the next
slide:
The properties of the vehicle are also applicable to the leaf
nodes. For example every vehicle has to have an engine for
moving which holds good for either “ Kawasaki Bajaj” or
“ Challenger”. Also that a vehicle’s purpose is for
transportation which is true either for a “Eicher Mitsubishi” or
“TGV” of France. This property of inheritance helps in
jumping from one level to another carrying the
characteristics of the generic node to very specific instances.

Vehicle

Land-Vehicle

Road –
Vehicle

Kawasaki
Bajaj

Water-Vehicle

Rail-Vehicle

Eicher
Mitsubishi

Shatabdi
Express

RiverVehicle

TGV of
France

Air-Vehicle

Sea-Vehicle

Canoe/boat

INS
Vikrant

Aircraft

Space-Vehicle

IAF’s
Baaz

Sputnik

F-16

Fig: How is_a link generates a hierarchical structure in a network.
Vx (Road_Vehicle(x)  land_Vehicle(x))

Challenger

Reasoning using semantic networks:
1. Specify the Start Node.
2. From the initial node, other nodes are pursued using the links until the
final node is reached.
Illustration: To illustrate this consider semantic network of Delhi University
Computer Centre, If one wishes to find “ What is the speed of the line
printer?”
1. identify the arc that has the characteristics “Speed” and
2. Find to what node does arc points to.
In our example we had given it a variable value which could be a numeric
one. This type of arriving at results by matching nodes and arcs and the
utilization of inheritance property helps to a great extent in the reasoning
process.
The major hurdle in utilizing semantic networks is that there is no
standardization and formalization as far as notations and reasoning is
concerned.
But the overall concept of arcs and nodes in semantic networks has been
standardized.

Links in a Semantic Net
• Basic types of links-relationship are:

• Isa link: representing the inclusion relationship of an object in
another(i.e. to link a class and its superclass).Ex: Bird isa
mammal.
• Has a part link: an object is described by another object(Bird
has-part Wings and Bird has-part Feather)
• Instance link: represent the relationship between a type and
a token (sparrow is an instance of bird)
• A node can have any number of superclasses that contain it.
• A node can be inherited by the properties of multiple parent
nodes and there ancestors in the network. It can cause the
conflict inheritance.

Example
Animal
isa
Bird

hasPart

isa
Robin

isa
Rusty

isa

Red

Wings

Example
Sue

mother

John

age

father

5

mother (john, sue)
age (john, 5)
wife (sue, max)

34

Max

age (max, 34)
Father(john,Max)

Example(Contd.)
Mammal
isa

Has-part
Person

Uniform
color
Blue

Nose

Instance
team
Sachin

India

Non-binary predicate representation
• Semantic nets are the natural way to represent the relationship of binary
predicates in predicate logic.
• Some Binary predicate logics from last example are:
• Isa(Person,Mammal)
• Instance(Sachin,Person)
• Uniform color(Sachin,Blue)
• Unary predicate logics can also be represented in the binary predicates by
using general purpose predicates, like isa and instance.Example,
• mammal (person),
• It can be represented as
• isa(Person,Mammal)
• Now its easy to represent it in the semantic net.

Non-binary pred. rep.(contd..)
• Three or more place predicates can also be converted to the binary form
by creating one new object representing the entire predicate statement.
• Example: score (INDIA ,USA ,7-2)
• It can be represented in the semantic net by creating an anote node
represent the specific game and then by relating the pieces of information
as follows:
Game

isa
INDIA

Visiting
team

GT

score

Home team
USA

7-2

• Example (1): John gave the book to Mary.

Give

Book

instance

agent
John

EV11

instance

object

beneficiary

Mary

BK22

Example (2):- Semantic Net
Mammal

Is_a

Person

has_part

Brain

instance

Blue

Uniform_color

Tendulkar

team

India

In this network inheritance has been used to derive additional relation.
has_part(Tendulkar,Brain)
INTERSECTION SEARCH:- Semantic nets can be used to find
relationships among objects by spreading activation and from each of two
nodes.
Q. What is the relationship between Tendulkar and Blue?
Representing: Semantic nets are the natural way to represent relationships
that would appear as ground instances of binary priadicates in pradicate
logic.
is_a( person, Mammal)
instance(Tendulkar,Person)
Predicates can be thought of as a binary predicates using some general
purpose predicates:
person (Tendulkar) can be instance(Tendulkar,person).
Then three or more predicates can be converted to a binary form by
creating one new object representing the entire predicate statement.

Eg.: Score(England,India,250-300)

Game
Is_a

250-300

score

Cricket

visiting_team

England

home_team

India

 A new node can be added to represent a specific game.
 Binary predicate like instance(Cricket,game).

Eg.: The following network describes a certain number of relations and
rules which are implicitly contained in the sentence .
Tendulkar has a car and blue cap.

Note: 1. The semantics of the arcs is not precise i.e. a car
is “a kind of “ vehicle but a man “is a” mammal.
ako ----- inheritance ----- Specialization.
Is_a -----------generalization.
2. But few relations are not in these three categories.
3. If the knowledge gets complicated then network
becomes complex.
4. Attempts have been made to make the semantics of
networks precise and to define a set of primitives for
representation called inheritance.
activity
Mammal

Man

ako
Car

Cat

colour

Is_a
Instance of

Vehicle

Tendulkar

owns

Cap

colour
Blue

There should be difference between a link that defines a new entity and
one that relates two existing entities.

a)

b)

height

John

John

72

Bill

height

height
greater_than

H1

H2

c)

John

Bill

height

height
greater_than

H1

H2
value

72

Conceptual Graphs
• Conceptual graphs are semantic nets representing the meaning of (simple)
sentences in natural language
• It is a technique for representing the content of a declarative sentence
describe the several aspects of a particular event.
• It contains the two types of nodes
– Concept node
– Relation node( binary relation between concepts)
• Example:

GO
NEW YORK

JOHN
Who

How

BUS

Where

Conflict due to multiple inheritance
In some semantic networks, one class can inherit properties of more than one
superclass.
The Ni o dia o d e a ple: It is idel a epted that Quake s te d to e
pacifists, and Republicans tend not to be. Nixon is known to be both - a
Quaker, and a Republican.
Pacifists

isa

Not isa

Quakers

republican
isa
Nixon

isa

The resulting conflict can be resolved only if additional information stating
a preference to one of the conflicting inferences is provided.

Partitioned Semantic Net
Breaks the semantic net in hierarchal manner (set of spaces).

Delhi University

Delhi College of
Engineering
C1

NSIT
C2

State board dept.
C3

• We can represent the simple quantified expressions in the form of
semantic net into a hierarchical set of spaces.
• Let us take a simple example :
– The dog bite the mail carrier.

Dogs

d

Is-a
assailant

Bite

Mail-carrier

Is-a

b

Is-a
victim

m

Partitioned Semantic Net(contd..)
• In this example the nodes dog, bite and male carrier represent the classes
of dogs , bitings, mail carriers respectively, while the node d, b, and m,
represent a particular dog, particular biting, and a particular mail carrier.
This is represented by a single net without partitioning.
• Let us we want to express the fact that
– Every dog has bitten a mail carrier.
– In Logic :

x : Dog(x) - Эy : Mail-Carrier(y) Λ Bite(x,y)
To represent this fact we need an universally quantified variable. This
can be done by using partitioning.

Partitioned Semantic Net(contd..)
SA
GS
Is-a
g

Dogs

form

Bite

Mail-carrier

Is-a
Is-a
S1 Is-a
assailant
victim
d
b
m

• Where g is an instance of specila class GS of
general statement about the world.

Semantic Net for :

Every dog in town has bitten the constable.

SA

Dogs
GS
Is-a
g

Town-Dogs

form

Bite

Constables

Is-a
S1 Is-a
assailant
victim
d
b

Is-a
C

Partitioned Semantic Net(contd..)
• Every dog has bitten every mail carrier.

SA

Dogs

Bite

Is-a

S1
d

Mail-carrier
Is-a

assailant

victim

b
form

GS

Is-a g

Is-a
m

Partitioned Semantic Net(contd..)
• In this case g have the two links, one pointing to d, which represent any
dog and one pointing to m, representing any mail carrier.
• SI space is at the lower level than the space SA. Because the nodes in SI
space represent to the particular objects.
• This representation is important because d does not stand for a particular
dog , it is basically a variable that represents dog.

Advantaages





It is very easy to visualize.
Abstract knowledge can be represent by linking them together.
Attributes can inherit in any object very easily.
Unary , binary, and more place predicate can be easily represented
through semantic net.
• It is efficient in the space requirements
– Object represent only once
– Relationship handled by the arc (pointers).

Disadvantages
• Facts placed inappropriately may cause the misconception.
• There is no standard about node and arc values.
• Multiple inheritance (Particularly from multiple sources when inheritance
are wanted) can cause the conflict.
• There is no standard definition of link and node names. This make it
diffi ult to u de sta d the et o k that’s h it is ot desig ed i the
consistent manner.
• Initially semantic network was proposed as a modal of human associative
memory. But the human brain contain 1010 neurons and 1015 links.
Co side ho lo g it take fo the hu a to a s e NO to a uestio
A e the e t ee o the oo ? O e iousl hu a p o ess i fo atio is i
very different way. It is not as modeled by the semantic net.

THANK YOU

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