2. Inheritable knowledge
• The relational knowledge base determines a set of
attributes and associated values that together
describe the objects of knowledge base.
E.g. Player_info(“john”,”6.1”,180,right_throws)
• The knowledge about the objects, their attributes and
their values need not be as simple as shown.
• One of the most powerful form of inference
mechanisms is property inheritance.
Player Height Weight Bats_throws
John 6.1 180 Right_throws
Sam 5.10 170 right_right
Jack 6.2 215 Bats_throws
3. • Property Inheritance
• Here elements of specific classes inherit attributes
and values from more general classes in which
they are included.
• In order to support property inheritance objects
must be organized into classes and classes must be
arranged in generalization hierarchy.
4. and boxed nodes== object/values of attriibutesof an objjectt..
person(Owen) instance(Owen, Person)
team(Owen, Liverpool)
Here,
Lines ==attributes
This structure is also called as slot and filler structure. These structures are the
devices to support property inheritance along isa and instance links.
Mammal
P
P
e
e
r
r
s
s
o
o
n
n
Owen
Nose
Red Liverpool
isa
instance
h
h
a
a
s
s
-
-
p
p
a
a
r
r
t
t
uniform
colour team
5. • Advantage of slot and filler structures:
1. monotonic reasoning can be performed more
effectively than with pure logic and non monotonic
reasoning is easily supported.
2. Makes it easy to describe properties of relations.
e.g. “does Owen has-part called nose?”
3. Form of object oriented programming and has
advantages such as modularity and ease of viewing
by people.
6. Slot and filler structures
Weak slot and filler structure Strong slot and filler structure
Frames
Semantic nets Scripts
Conceptual Dependency
Weak slot and filler structures: are “Knowledge- Poor” or
“weak” as very little importance is given to the specific
knowledge the structure should contain.
Attribute= slot and its value= filler
7. Semantic nets
• In semantic nets information is represented as:
– set of nodes connected to each other by a set of
labelled arcs.
• Nodes represent: various objects / values of the
attributes of object .
• Arcs represent: relationships among nodes.
Mammal
Person
Jack
Nose
Blue Chicago Royals
isa
instance
has-part
uniform
color team
8. • In this network we could use inheritance to derive
the additional info:
has_part(jack, nose)
Intersection Search
One way to find relationships among objects is to spread
the activation(links) out from two nodes and find out
where it meets
Ex: relation between :
Red and liverpool
Mammal
Person
Owen
Nose
Red Liverpool
isa
instance
has-part
uniform
color team
9. • Representing non binary predicates:
1. Unary –
e.x. Man(marcus) can be converted into:
instance(marcus,Man)
2. Other arities-
e.x. Score(india,australia,4-1)
3 or more place predicates can be converted to binary
form as follows:
1. Create new object representing the entire
predicate.
2. Introduce binary predicates to describe relation to
this new object.
11. Ex. 2. “john gave the book to Mary”
give(john,mary,book)
EV 1
instance
Give
John
Mary
Book
BK1
instance
Object
agent
beneficiary
12. Making some important distinctions
1. “john has height 72”
2. “john is taller than Bill”
John 72
height
John Bill
H1 H2
height height
Value
72
greater_than
13. Partitioned semantic nets
• Used to represent quantified expressions in
semantic nets.
• One way to do this is to partition the semantic net
into a hierarchical set of spaces each of which
corresponds to the scope of one or more variable.
• “the dog bit the mail carrier” [partitioning not required]
d
Dogs
b
Bite
m
Mail-Carrier
isa isa isa
assailant victim
14. • “every dog has bitten a mail carrier”
x: dog (x) y: mail-carrier(y) bite(x, y)
• How to represent universal quantifiers?
– Let node ‘g’ stands for assertion given above
– This node is an instance of a special class ‘GS’ of
general statements about the world.
– Every element in ‘GS’ has 2 attributes:-
• Form - states relation that is being asserted.
• connections - one or more, one for each of the universally
quantified variables.
– ‘SA’ is the space of partitioned sementic net.
15. • “every dog has bitten a mail carrier”
SA
S1
d
Dogs
b
Bite
m
Mail-Carrier
isa isa isa
assailant victim
g
GS
isa
form
16. • “Every dog in the town has bitten the constable”
SA
m
Constables
isa
S1
d
Dogs
b
Bite
isa isa
assailant
g
GS
isa
form
victim
17. • “Every dog in the town has bitten every constable”
SA
Constables
S1
d
Dogs
b
Bite
isa isa
assailant
g
GS
isa
form
victim
c
isa
18. • More examples of sementic nets:
• “ Mary gave the green flowered vase to her
favourite cousin”
EV 1
instance
Give
Mary
cousin
vase
Object
agent
beneficiary
Colour_pattern
Green
flowered
favourite
19. • “every batsman hits a ball”
SA
S1
b
Batsman Hits
b
Balls
isa isa isa
action Acts_on
g
GS
isa
form
h
20. • “Tweety is a kind of bird who can fly. It is Yellow
in colour and has wings.”
Bird
Tweety
Wings
instance
has-part
yellow
fly
colour
action
21. • Represent following using sementic nets:-
Tom is a cat. Tom caught a bird. Tom is owned by John. Tom is
ginger in color. Cats like cream.The cat sat on the mat. Acat is
a mammal. Abird is an animal. All mammals are
animals.mammals have fur.
22. Frames
• Another kind of week slot and filler structure.
• Frame is a collection of attributes called as slots
and associated values that describe some entity in
the world (filler).
• Consider,
Room
Hotel room
isa
Hotel bed
contains
Hotel Chair
contains
Location
Chair
Sitting_on
4
20-40 cms
isa
use
legs
height
Room No 2
instance
23. Hotel Room
isa : Room
contains: Hotel Bed
contains: Hotel Chair
Hotel Chair
isa: Chair
use: sitting_on
location: Hotel Room
. . . . . . . . . .
. . . . . . . . . .
. . . . . . . . . .
Frame System for Hotel Room
Frame structure for Hotel Room
Frame structure for Hotel Chair
Frame structure for all remaining
attributes
24. Person Jack_Roberts
instance: Fielder
height: 5-10
balls: right
batting_avg: 0.309
team: Chicagocubs
uniform_color: blue
Fielder
isa: ML_Baseball_Player
cardinality: 376
batting_avg: 0.262
ML_Baseball_Team
isa:T
eam
cardinality: 26
team_size: 24
manager:
isa: Mammal
cardinality: 6,000,000,000
* Handed: right
Adult Male
isa: Person
Cardinality: 2,000,000,000
* Height: 5- 10
ML_Baseball_Player
isa:Adult_Male
cardinality: 624
* height: 6-1
* bats: equal tohanded
* batting-avg: 0.252
* team:
*uniform_color:
Individual
frame
25. • Meta Class: special class whose elements themselves are classes.
– If X is meta class and Y is another class which is an element of X, then Y inherits
all the attributes of X.
• Other ways of relating classes to each other
1. Mutually disjoint: 2 classes are mutually disjoint if they are
guaranteed to have no elements in common.
2. Is covered by: relationship is called as ‘covered-by’ when we have
a class and it has set of subclasses, the union of which is equal to the
superclass.
ML_Baseball_Player
isa
Fielder Pitcher
isa
Catcher
isa
American
Leaguer
isa
National
Leaguer
isa
Jack
instance
instance
27. Tangled Hierarchies
• Hierarchies that are not trees
• Usually hierarchy is an arbitrary directed acyclic
graph.
• Tangled hierarchies requires new property
inheritance algorithm.
28. •
FIGURE A
• Can fifi fly?
• The correct answer must be ‘no’.
– Although birds in general can fly, the subset of birds , ostriches does not.
– Although class pet bird provides path from fifi to bird and thus to the answer that fifi
can fly, it provides no info that conflicts with the special case knowledge associated with
class ostrich, so it should hove no effect on the answer.
isa
isa
isa
isa
Ostrich
fly :no
fifi
fly : ?
Bird
fly :yes
Pet-Bird
29. FIGURE B
• Is Jack Pacifist?
– Ambiguity
• One way to solve ambiguity is to base the new inheritance algo based on path
length:
– Using BFS start with the frame for which slot value is needed.
– Follow its instance links, then follow isa links upwards .
– If the path produces a value it can be terminated, as can all other paths once their length
exceeds that of the successful path.
instance
instance Jack
Pacifist : ?
Quaker
pacifist: true
Republican
pacifist: False
30. • Using this technique our answers to fifi problem is :’no’ and
for jack problem we get 2 values hence ‘contradiction’.
• Now consider following hierarchies:
FIGURE C
• Our new algo gives answer: fifi can fly. i.e. Fly: yes.
isa
instance
instance
isa
White-Plumed
Ostrich
fifi
fly : ?
Bird
fly :yes
Pet-Bird
Plumed Ostrich
isa
Ostrich
fly: no
isa
31. FIGURE D
• Here our new algo reaches Quaker and
deduces pacifist:true and stops without
noticing further contradiction.
instance
instance
Jack
Pacifist : ?
Quaker
pacifist: true
Conservative
Republican
isa
Republican
pacifist: false
32. • Solution to the problem is to base our algo not
based on path length but on inferential distance.
• Class 1 is closer to class2 than class 3 if class1 has an
inferential path through class2 to class3.
• For figure A answer is no
• For figure B Contradiction
PLEASE REFER PROPERTY INHERITANCE ALGO
Rich and Knight pg. 204-205 3rd edition.
Class 2
Class 1 Class 3
