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![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](https://image.slidesharecdn.com/moduleii-150324112034-conversion-gate01/75/weak-slot-and-filler-structure-13-2048.jpg)
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weak slot and filler structure
- 1. WEAKSLOT-ANDFILLER-STRUCTURES Amey D.S.Kerkar, Asst.Professor, ComputerEngineering Dept. Don Bosco College of Engineering, Fatorda-Goa.
- 2. Inheritable knowledge • Therelational 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 andboxed 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 ofslot 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 fillerstructures 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 • Insemantic 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 thisnetwork 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 nonbinary 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.
- 10. TEST4 isa Game Australia India 4-1 Visiting _team Score Home_team Score(india,australia,4-1)
- 11. Ex. 2. “johngave the book to Mary” give(john,mary,book) EV 1 instance Give John Mary Book BK1 instance Objectagent beneficiary
- 12. Making some importantdistinctions 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 doghas 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 doghas bitten a mail carrier” SA S1 d Dogs b Bite m Mail-Carrier isa isa isa assailant victim g GS isa form
- 16. • “Every dogin 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 dogin the town has bitten every constable” SAConstables S1 d Dogs b Bite isa isa assailant gGS isa form victim c isa
- 18. • More examplesof 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 batsmanhits a ball” SA S1 b Batsman Hits b Balls isa isa isa action Acts_on g GS isa form h
- 20. • “Tweety isa 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 followingusing 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 kindof 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: Mammalinstance: 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 • Hierarchiesthat are not trees • Usually hierarchy is an arbitrary directed acyclic graph. • Tangled hierarchies requires new property inheritance algorithm.
- 28. • FIGURE A • Canfifi 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 • IsJack 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 thistechnique 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 • Hereour 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 tothe 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