Artificial intelligence

​

UNIT-3

KVSM, Dept of CSE , SRKREC

Department of Computer Science and Engineering

S R K R Engineering College,

Bhimavaram-543204

KVSM, Dept of CSE , SRKREC

The basic idea of Semantic nets is that how it carries the meaning of the concept and how the concepts are related to other topics.

Semantic nets consists of nodes and these nodes are connected by labeled edges.

In the Semantic nets :

• Nodes are represented as circles or rectangles
• Labeled link are used to connect the nodes

In Semantic nets, there are two important attributes between the nodes.

• IS A : Denotes sub class relationship
• INSTANCE : Denotes class membership

​

Semantic Nets

KVSM, Dept of CSE , SRKREC

Example for Semantic Net

Is a

Is a

Is a

Instance

Instance

have

like

Sat on

In color

Owned by

caught

Consider the following facts:

Tom is a cat

Tom caught jack

Tom is owned by john

Tom is in ginger color

The cat sat on mat

Cat like cream

Cat is mammal

Jack is a rat

Rat is animal

All animals are mammals

Mammals have fur

Rat

KVSM, Dept of CSE , SRKREC

Features of Semantic nets

Is a

Is a

Is a

Instance

Instance

have

like

Sat on

In color

Owned by

caught

Semantic nets are natural way of representing non binary relationships that would appear in the nets.

Isa ( cat, Mammal)

Is a( rat, Animal)

Isa ( Animal, Mammal)

Instance( Tom, Cat)

Instance(Jack, Rat)

Like(Cat, Cream)

Saton (Cat, Mat)

Rat

Representing as binary predicates

KVSM, Dept of CSE , SRKREC

Features of Semantic nets

Consider the sentence:

“John gave the AI book to Mary”

​

Now the binary predicates:�Instance( G25,Give)

agent (G25, John)

Beneficiary(G25,Mary) Object( G25,AI book)

Instance(AI book, book)

Representing the facts

Instance

Agent

Beneficiary

Object

instance

KVSM, Dept of CSE , SRKREC

Features of Semantic nets

Consider the sentence:

“The score in the match between Chicago and Brooklyn is 5-3”

The non binary predicate:

SCORE(Chicago,brooklyn,5-3)

​

Now the binary predicates:�Instance( G25,Game)

Home team (G25,Chicago )

Visiting team(G25,Brooklyn)

Score(G25,5-3)

Representing Non binary predicates

Instance

Home

Team

Visiting

Team

Score

KVSM, Dept of CSE , SRKREC

Features of Semantic nets

Intersection search

KVSM, Dept of CSE , SRKREC

Features of Semantic nets

Intersection search

KVSM, Dept of CSE , SRKREC

Features of Semantic nets

KVSM, Dept of CSE , SRKREC

Features of Semantic nets

Another example : Making important distinctions

John taller than bill

KVSM, Dept of CSE , SRKREC

Example of Semantic nets

Instance

IS a

IS a

Instance

Instance

Owned by

Color

Moti is a dog

Moti is owned by john

Moti in white color

John is person

Dog is an animal

Animals are mammas

KVSM, Dept of CSE , SRKREC

Example of Semantic nets

Instance

KVSM, Dept of CSE , SRKREC

Knowledge Representation Schemes

Boxed nodes : objects and values of attributes of objects.

​

Arrows : The point from object to its value.

​

This structure is known as a slot and filler structure, semantic network or a collection of frames.

Inheritable Knowledge

KVSM, Dept of CSE , SRKREC

The property inheritance Algorithm to retrieve a value for an attribute of an instance object:

1. Find the object in the knowledge base

2. If there is a value for the attribute report it

3. Otherwise look for a value of an instance, if none fail

4. Also, Go to that node and find a value for the attribute and then report it

5. Otherwise, search through using is until a value is found for the attribute.

Inheritable Knowledge

KVSM, Dept of CSE , SRKREC

Team(Brown)= Chicago

Team (PWReese)=Brooklyn

​

Bating avg (Brown)=0.106

​

Height(PWReese)=195

​

Handed(Brown)=right

Inheritable Knowledge

KVSM, Dept of CSE , SRKREC

Hendrix developed partitioned semantic network which allow the nodes and arcs are to be grouped together to form spaces.

Partitioned semantic networks

1. The dog bit the mail carrier.

​

• Every dog has bitten a mail carrier

​

​

• Every dog has bitten the constable

​

​

• Every dog has bitten every mail carrier

​

KVSM, Dept of CSE , SRKREC

Consider the sentence:

Danny the dog, bit the postman peter

Partitioned semantic networks

KVSM, Dept of CSE , SRKREC

Consider the sentence:

Every dog has bitten a postman

Partitioned semantic networks

KVSM, Dept of CSE , SRKREC

Consider the sentence:

Every dog in town has bitten the postman

Partitioned semantic networks

KVSM, Dept of CSE , SRKREC

Consider the sentence:

Every dog has bitten every postman

Partitioned semantic networks

KVSM, Dept of CSE , SRKREC

Consider the sentence:

John believes that pizza is tasty

Partitioned semantic networks

KVSM, Dept of CSE , SRKREC

Consider the sentence:

Every student loves to party

Partitioned semantic networks

KVSM, Dept of CSE , SRKREC

Advantages of Partitioned semantic network:

• Easy to visualize and understand
• The knowledge is defined as relationships.
• The knowledge is categorized.
• Related Knowledge is clustered.
• Efficient space utilization.

​

Partitioned semantic networks

KVSM, Dept of CSE , SRKREC

Disadvantages of Partitioned semantic network:

• Inheritance particularly from multiple source may cause the inconsistency problems.
• No standards about arcs, their labels and values.
• The knowledge is categorized.
• Related Knowledge is clustered.
• Efficient space utilization.

​

Partitioned semantic networks

KVSM, Dept of CSE , SRKREC

25

​

Thank you

​

KVS MURTHY

Assistant Professor

Department of CSE

SRKR Engineering College

9848290433

kvssrmurthy75@gmail.com

kvssr.murthy@srkrec.edu.in