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WEAKSLOT-ANDFILLER-STRUCTURES
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.
Advantage of slot and
filler structures:
1. It enables attribute values to be retrieved quickly.
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.
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.
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)
Semantic network of
properties of ice and snow
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- the number of arguments that the function
takes.
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
Advantage
• Easy to represent in a semantic net
• Useful for representing the contents of a typical declarative
sentence that describes several aspects of a particular event.
Making some important
distinctions
“john has height 72”
John 72
height
There should be a difference between a link that defines a
new entity and one that relates two existing entities
Making some important
distinctions
“john is 6 feet tall and he is taller than Bill”
John Bill
H1 H2
height height
72
Value
greater_than
• Node – Object – Concept
• Link – Attribute- Arc
• Value - Value- Value
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.
a) “the dog bit the mail carrier” [partitioning not required]
• Assailant- a person who physically attacks another.
b) “every dog has bitten a mail carrier”
c) “Every dog in the town has bitten the constable”
d) “Every dog in the town has bitten every constable”
• “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 semantic net.
Dannythe dog bit peterthe postman
•
• “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
Advantages of Semantic
Networks
• Easy to visualise and understand.
• The knowledge engineer can arbitrarily
defined the relationships.
• Related knowledge is easily categorised.
• Efficient in space requirements.
• Node objects represented only once.
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
Frames
• Frames can be classes (Person, Adult-Male, ML-Baseball-
Player, Fielder, ML-Baseball-Team) or instances (Pee-Wee-
Reese, Brooklyn –Dodgers)
• Class- isa relation- subset- subclasses
• Instance- instance- element of
• Since Class represents set- Two different types of attributes
associated with it- Attributes about the set itself and
attributes that are inherited by each element of the set.
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
• 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
Slot as full fledged objects
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 also 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 also 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
Class 2Class 1 Class 3

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weak slot and filler

  • 2. 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.
  • 3. Advantage of slot and filler structures: 1. It enables attribute values to be retrieved quickly. 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.
  • 4. 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
  • 5. 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.
  • 6. 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)
  • 8. 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- the number of arguments that the function takes. 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. Advantage • Easy to represent in a semantic net • Useful for representing the contents of a typical declarative sentence that describes several aspects of a particular event.
  • 13. Making some important distinctions “john has height 72” John 72 height There should be a difference between a link that defines a new entity and one that relates two existing entities
  • 14. Making some important distinctions “john is 6 feet tall and he is taller than Bill” John Bill H1 H2 height height 72 Value greater_than
  • 15. • Node – Object – Concept • Link – Attribute- Arc • Value - Value- Value
  • 16. 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. a) “the dog bit the mail carrier” [partitioning not required] • Assailant- a person who physically attacks another. b) “every dog has bitten a mail carrier” c) “Every dog in the town has bitten the constable” d) “Every dog in the town has bitten every constable”
  • 17. • “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 semantic net.
  • 18.
  • 19. Dannythe dog bit peterthe postman •
  • 20.
  • 21.
  • 22.
  • 23.
  • 24. • “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
  • 25. Advantages of Semantic Networks • Easy to visualise and understand. • The knowledge engineer can arbitrarily defined the relationships. • Related knowledge is easily categorised. • Efficient in space requirements. • Node objects represented only once.
  • 26. 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
  • 27. 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
  • 28. Frames • Frames can be classes (Person, Adult-Male, ML-Baseball- Player, Fielder, ML-Baseball-Team) or instances (Pee-Wee- Reese, Brooklyn –Dodgers) • Class- isa relation- subset- subclasses • Instance- instance- element of • Since Class represents set- Two different types of attributes associated with it- Attributes about the set itself and attributes that are inherited by each element of the set.
  • 29.
  • 30. 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
  • 31. Meta Class • 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.
  • 32.
  • 33.
  • 34. 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
  • 35.
  • 36. Slot as full fledged objects
  • 37.
  • 38. Tangled Hierarchies • Hierarchies that are not trees • Usually hierarchy is an arbitrary directed acyclic graph. • Tangled hierarchies requires new property inheritance algorithm.
  • 39. • 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
  • 40. FIGURE B • Is Jack Pacifist? • Ambiguity • One way to solve ambiguity is to base the new inheritance also 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
  • 41. • 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
  • 42. FIGURE D • Here our new also reaches Quaker and deduces pacifist:true and stops without noticing further contradiction. instanceinstance Jack Pacifist : ? Quaker pacifist: true Conservative Republican isa Republican pacifist: false
  • 43. • 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 Class 2Class 1 Class 3