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WEAKSLOT-ANDFILLER-STRUCTURES
Amey D.S.Kerkar,
Asst.Professor, Computer Engineering Dept.
Don Bosco College of Engineering,
Fatorda-Goa.
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
• 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.
Here,
Lines ==attributes and boxed nodes== object/values of attributes of an object.
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
Person
Owen
Nose
Red Liverpool
isa
instance
has-part
uniform
colour team
person(Owen)  instance(Owen, Person)
team(Owen, Liverpool)
Here,
Lines ==attributes and boxed nodes== object/values of attributes of an object.
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
Person
Owen
Nose
Red Liverpool
isa
instance
has-part
uniform
colour team
• 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.
Slot and filler structures
Attribute= slot and its value= filler
Weak slot and filler structure Strong slot and filler structure
FramesSemantic nets ScriptsConceptual 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.
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
• 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
• 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.
TEST4
isa
Game
Australia
India
4-1
Visiting _team Score
Home _team
Score(india,australia,4-1)
Ex. 2. “john gave the book to Mary”
give(john,mary,book)
EV 1
instance
Give
John
Mary
Book
BK1
instance
Objectagent
beneficiary
Making some important distinctions
1. “john has height 72”
2. “john is taller than Bill”
John 72
height
John Bill
H1 H2
height height
72
Value
greater_than
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
• “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.
• “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

• “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
• “Every dog in the town has bitten every constable”
SAConstables
S1
d
Dogs
b
Bite
isa isa
assailant
gGS
isa
form 
victim
c
isa

• More examples of sementic nets:
• “ Mary gave the green flowered vase to her
favourite cousin”
EV 1
instance
Give
Mary
cousin
vase
Objectagent
beneficiary
Colour_pattern
Green
flowered
favourite
• “every batsman hits a ball”
SA
S1
b
Batsman Hits
b
Balls
isa isa isa
action Acts_on
g
GS
isa
form

h
• “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
• 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.
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
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
Person Jack_Roberts
isa: Mammal instance: Fielder
cardinality: 6,000,000,000 height: 5-10
* Handed: right balls: right
batting_avg: 0.309
Adult__Male team: Chicago cubs
isa: Person uniform_color: blue
Cardinality: 2,000,000,000 Fielder
* Height: 5- 10 isa: ML_Baseball_Player
cardinality: 376
ML_Baseball_Player batting_avg: 0.262
isa: Adult_Male
cardinality: 624 ML_Baseball_Team
* height: 6-1 isa: Team
* bats: equal to handed cardinality: 26
* batting-avg: 0.252 team_size: 24
* team: manager:
*uniform_color:
Individual
frame
• 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
instanceinstance
ML_Baseball_Player
is covered-by: { Pitcher, Catcher, Fielder,American leaguer, National leaguer}
Pitcher
isa: ML_Baseball_Player
mutually_disjoint-with: {Catcher, Fielder }
Catcher
isa: ML_Baseball_Player
mutually_disjoint-with: {Pitcher, Fielder }
Fielder
isa: ML_Baseball_Player
mutually_disjoint-with: {Pitcher, Catcher}
.
.
.
.
Tangled Hierarchies
• Hierarchies that are not trees
• Usually hierarchy is an arbitrary directed acyclic
graph.
• Tangled hierarchies requires new property
inheritance algorithm.
•
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
isaisa
isa
Ostrich
fly :no
fifi
fly : ?
Bird
fly :yes
Pet-Bird
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.
instanceinstance Jack
Pacifist : ?
Quaker
pacifist: true
Republican
pacifist: False
• 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
FIGURE D
• Here our new algo reaches Quaker and
deduces pacifist:true and stops without
noticing further contradiction.
instanceinstance
Jack
Pacifist : ?
Quaker
pacifist: true
Conservative
Republican
isa
Republican
pacifist: false
• 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 2Class 1 Class 3
weak slot and filler structure

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weak slot and filler structure

  • 1. WEAKSLOT-ANDFILLER-STRUCTURES Amey D.S.Kerkar, Asst.Professor, Computer Engineering Dept. Don Bosco College of Engineering, Fatorda-Goa.
  • 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. Here, Lines ==attributes and boxed nodes== object/values of attributes of an object. 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 Person Owen Nose Red Liverpool isa instance has-part uniform colour team person(Owen)  instance(Owen, Person) team(Owen, Liverpool) Here, Lines ==attributes and boxed nodes== object/values of attributes of an object. 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 Person Owen Nose Red Liverpool isa instance has-part 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 Attribute= slot and its value= filler Weak slot and filler structure Strong slot and filler structure FramesSemantic nets ScriptsConceptual 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.
  • 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 Objectagent 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 72 Value 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” SAConstables S1 d Dogs b Bite isa isa assailant gGS 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 Objectagent 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 isa: Mammal instance: Fielder cardinality: 6,000,000,000 height: 5-10 * Handed: right balls: right batting_avg: 0.309 Adult__Male team: Chicago cubs isa: Person uniform_color: blue Cardinality: 2,000,000,000 Fielder * Height: 5- 10 isa: ML_Baseball_Player cardinality: 376 ML_Baseball_Player batting_avg: 0.262 isa: Adult_Male cardinality: 624 ML_Baseball_Team * height: 6-1 isa: Team * bats: equal to handed cardinality: 26 * batting-avg: 0.252 team_size: 24 * team: manager: *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 instanceinstance
  • 26. ML_Baseball_Player is covered-by: { Pitcher, Catcher, Fielder,American leaguer, National leaguer} Pitcher isa: ML_Baseball_Player mutually_disjoint-with: {Catcher, Fielder } Catcher isa: ML_Baseball_Player mutually_disjoint-with: {Pitcher, Fielder } Fielder isa: ML_Baseball_Player mutually_disjoint-with: {Pitcher, Catcher} . . . .
  • 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 isaisa 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. instanceinstance 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. instanceinstance 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 2Class 1 Class 3