![Property Inheritance Algorithm
Input: Object, and property to be found from Semantic Net;
Output:Yes, if the object has the desired property else return
No;
Procedure:
Find the object in the semantic net;
Found = false;
While [ (object ≠ root) AND not Found ] DO
{ If there is a property attribute attached with an object
then Found = true
else if object=inst(object, class) OR isa(object, class)
then object = class };
If Found = true then Report ‘Yes’ else report ‘No’;
15](https://image.slidesharecdn.com/ch7knowledgerepresentation-221101140031-bf76bac4/85/Ch-7-Knowledge-Representation-pdf-15-320.jpg)
Ch 7 Knowledge Representation.pdf
- 1. Ch 7 Knowledge Representation Knowledge representation (KR) is an important issue in both cognitive science and artificial intelligence. − In cognitive science, it is concerned with the way people store and process information and − In artificial intelligence (AI), main focus is to store knowledge so that programs can process it and achieve human intelligence. There are different ways of representing knowledge e.g. − predicate logic, − semantic networks, − extended semantic net, − frames, − conceptual dependency etc. In predicate logic, knowledge is represented in the form of rules and facts as is done in Prolog. 1
- 2. Knowledge Representation In AI, knowledge representation is an important area because intelligent problem solving can be achieved and simplified choosing an appropriate KR technique. Representing knowledge in some specific ways makes certain problems easier to solve. Since knowledge is utilized to achieve intelligent behaviour, the fundamental goal of KR is to represent knowledge in a manner that facilitates the process of inferencing (i.e., drawing conclusions) from it. 2
- 3. Knowledge Representation Any KR system should possess properties such as learning, efficiency in acquisition, representational adequacy and inferential adequacy. Learning: by reasoning, by experience, by observation and scientific methods. Efficiency in acquisition: the ability to acquire new knowledge through automatic methods without human intervention. Representational adequacy: the ability to represent the required knowledge. Inferential adequacy: the ability to infer new knowledge. 3
- 4. Approaches to KR Relational Knowledge – Database systems Logic – PL – FOL Procedural Knowledge – Knowledge encoded in the form of procedures that carry out specific tasks based on relevant knowledge. • Compilers • Interpreters 4
- 5. Inheritable knowledge Having already seen logic as a knowledge representation in chapter 4, we will now see inheritable knowledge representation mechanisms. – Semantic Networks – Extended Semantic Networks – Frames Also known as structured knowledge representation. 5
- 6. Semantic Networks The basic idea behind semantic networks is that the meaning of a concept is derived from its relationship with other concepts, and the information is stored by interconnecting nodes with labeled arcs. Inheritance in Semantic Net: – Hierarchical structure allows knowledge to be stored at the highest possible level of abstraction - reducing size. – In-built inheritance mechanism. – It is a natural tool for representing taxonomically structured information - sub-concepts of a concept share common properties. 6
- 7. Semantic Networks The syntax of semantic net is simple. It is a network of labeled nodes and links. – It’s a directed graph with nodes corresponding to concepts, facts, objects etc. and – arcs showing relation or association between two concepts. The commonly used links in semantic net are of the following types. – isa subclass of entity (e.g., child hospital is subclass of hospital) – inst particular instance of a class (e.g., India is an instance of country) – prop property link (e.g., property of dog is ‘bark) 7
- 8. Representation of Knowledge in Sem Net “Every human, animal and bird is a living thing who breathes and eats. All birds can fly. All men and women are humans who have two legs. John is a man. Cat is an animal and has fur. All animals have skin and can move. Giraffe is an animal who is tall and has long legs. Parrot is a bird and is green in color”. 8
- 9. Representation in Semantic Net Semantic Net breathe, eat Living_thing prop isa isa two legs isa fly Human Animal Bird isa isa isa isa isa prop green Man Woman Giraffe Cat Parrot prop prop prop inst fur john skin, move tall, long legs 9
- 10. Another example Tom is a cat. Tom caught a bird. Tom is owned by John. Tom is ginger in colour. Cats like cream. The cat sat on the mat. A cat is a mammal. A bird is an animal. All mammals are animals. Mammals have fur. 10 inst inst inst inst
- 11. More examples 11
- 13. Semantic networks It is argued that this form of representation is closer to the way humans structure knowledge by building mental links between things than the predicate logic we considered earlier. Note in particular how all the information about a particular object is concentrated on the node representing that object, rather than scattered around several clauses in logic. 13
- 14. Inheritance Inheritance mechanism allows knowledge to be stored at the highest possible level of abstraction which reduces the size of knowledge base. – It facilitates inferencing of information associated with semantic nets. – It is a natural tool for representing taxonomically structured information and ensures that all the members and sub- concepts of a concept share common properties. – It also helps us to maintain the consistency of the knowledge base by adding new concepts and members of existing ones. Properties attached to a particular object (class) are to be inherited by all subclasses and members of that class. 14
- 15. Property Inheritance Algorithm Input: Object, and property to be found from Semantic Net; Output:Yes, if the object has the desired property else return No; Procedure: Find the object in the semantic net; Found = false; While [ (object ≠ root) AND not Found ] DO { If there is a property attribute attached with an object then Found = true else if object=inst(object, class) OR isa(object, class) then object = class }; If Found = true then Report ‘Yes’ else report ‘No’; 15
- 16. Inheritance Bird Canary Is-a Yellow Tweety yes Sylvester inst can-talk? has-enemy has-color feathers body-covering Does Tweety have feathers? Yes. How do we know? He inherits that property from a superior node in the hierarchy. 16
- 17. Inheritance Can Opus fly? No. How do we know? He inherits that property from its closest node in the hierarchy. Bird Flightless bird is-a Penguin Opus is-a inst fly has-property can’t fly has-property Canary is-a 17
- 18. Assigned attributes Can Dumbo fly? Yes. How do we know? Local (assigned) attribute overrides inherited attribute Animal Mammal is-a Elephant Dumbo is-a inst can move has-property can’t fly has-property Bird is-a has-property can fly 18
- 19. Coding of Semantic Net in Prolog Isa facts Instance facts Property facts isa(living_thing, nil). isa(human, living_thing). isa(animals, living_thing). isa(birds, living_thing). isa(man, human ). isa(woman, human). isa(cat, animal). isa(giraffe, animal) isa(parrot, bird) inst(john, man). prop(breathe, living_thing). prop(eat, living_thing). prop(two_legs, human). prop(skin, animal). prop(move, animal). prop(fur, bird). prop(tall, giraffe). prop(long_legs, giraffe). prop(green, parrot). 19
- 20. Inheritance Rules in Prolog Instance rules: instance(X, Y) :- inst(X, Y). instance (X, Y) :- inst(X, Z), subclass(Z,Y). Subclass rules: subclass(X, Y) :- isa(X, Y). subclass(X, Y) :- isa(X, Z), subclass(Z, Y) . Property rules: property(X, Y) :- prop(X, Y). property(X, Y) :- instance(Y, Z), property(X, Z). property(X, Y) :- subclass(Y, Z), property(X, Z). 20
- 21. Queries ● Is john human? ● Is parrot a living thing? ● Is giraffe an aimal? ● Is woman subclassof living thing ● Does parrot fly? ● Does john breathe? ● has parrot fur? ● Does cat fly? ?- instance(john, humans). Y ?- subclass(parrot, living_thing). Y ?- subclass(giraffe, animal).Y ?- subclass(woman, living_thing). Y ?- property(fly, parrot). Y ?- property (john, breathe). Y ?- property(fur, parrot). N ?- property(fly, cat). N 21
- 22. Advantages of Semantic nets Easy to visualize Formal definitions of semantic networks have been developed. Related knowledge is easily clustered. Efficient in space requirements – Objects represented only once – Relationships handled by pointers 22
- 23. Problems with semantic nets There is no standard definition of link and node names. This makes it difficult to understand the network, and whether or not it is designed in a consistent manner. Initially, semantic nets were proposed as a model of human associative memory (Quillian, 1968). But, are they an adequate model? It is believed that human brain contains about 1010 neurons, and 1015 links. Consider how long it takes for a human to answer “NO” to a query “Are there trees on the moon?” Obviously, humans process information in a very different way, not as suggested by the proponents of semantic networks. 23
- 24. Problems with semantic nets … Semantic nets are logically and heuristically very weak. Statements such as “Some books are more interesting than others”, “No book is available on this subject”, “If a fiction book is requested, do not consider books on history, health and mathematics” cannot be represented in a semantic network. In general, semantic networks cannot represent – negation "John does not go fishing"; – disjunction "John eats pizza or fish and chips"; – clauses 24
- 25. 25 Exercises Try to represent the following two sentences into the appropriate semantic network diagram: – isa(person, mammal) – instance(Mike-Hall, person) all in one graph – team(Mike-Hall, Cardiff) – score(Spurs, Norwich, 3-1) – John gave Mary the book. – John gave Mary the book in the kitchen.
- 26. Extended Semantic Network In conventional Semantic Networks, clausal form of logic cannot be expressed. Easy to add information to Semantic Networks (not so in logic). Extended Semantic Network (ESNet) combines the advantages of both logic and semantic network. In the ESNet, terms are represented by nodes similar to semantic networks. Binary predicate symbols in clausal logic are represented by labels on arcs of ESNet. − An atom of the form “Love(john, mary)” is an arc labeled as ‘Love’ with its two end nodes representing ‘john’ and ‘mary’. Conclusions and conditions in clausal form are represented by different kinds of arcs. – Conditions are drawn with two lines (or ) and conclusions are drawn with one heavy line . 26
- 27. Examples Represent ‘grandfather’ definition Gfather(X, Y) Father(X, Z), Parent(Z, Y) in ESNet. Z Father Parent X Y Gfather 27
- 28. Another Example Represent clausal rule “Male(X), Female(X) Human(X)” using binary representation as “Isa(X, male), Isa(X, female) Isa(X, human)” and subsequently in ESNet as follows: male Isa Isa X human Isa female 28
- 29. Inference Rules in ESNet Inference rules are embedded in the representation itself. The inference that “for every action of giving, there is an action of taking” in clausal logic written as “Action(f(E), take) Action(E, give)”. 29 ESNet Action F(E) take Action E give
- 30. Inference Rules in ESNet The inference rule such as “an actor of taking action is also the recipient of the action” can be easily represented in clausal logic as: Recipient(E, Y) Action(E, take), Actor(E, Y) − Here E is a variable representing an event where an action of taking is happening. 30 ESNet Action E take Recipient Actor Y
- 31. Example Represent the following clauses of Logic in ESNet. Recipient(E, Y) Acton(E, take), Actor(E, Y) Object(e, apple). Action(e, take). Actor(e, john) . 31 apple Object e E Recipient Actor Action Actor Action john take Y
- 32. Contradiction The contradiction in the ESNet arises if we have the following situation. 32 Part_of P X Isa Part_of Y
- 33. Deduction in ESNet Both of the following inference mechanisms are available in ESNet. – Forward reasoning inference (bottom up approach) • Bottom Up Inferencing: Given an ESNet, apply the following reduction using modus ponens rule of logic (if {B, A B} then A). – Backward reasoning inference (top down approach). • Top Down Inferencing: Prove a conclusion from a given ESNet by adding the denial of the conclusion to the network and show that the resulting set of clauses in the network is inconsistent (using resolution). 33
- 34. Example: forward reasoning Given set of clauses Isa(X, human) Isa(X, man) Isa(john, man). Inferencing Isa(john, human) human Isa X Isa man john Isa Here X is bound to john human Isa john 34
- 35. Example: backward reasoning Given set of clauses Isa(X, human) Isa(X, man) Isa(john, man). Prove conclusion Query: Isa(john, human) denial of query human Isa X Isa man john Isa human Isa X Isa Isa man john Isa 35
- 36. Cont… human X = john Isa Isa john Contradiction or Empty network is generated. Hence “Isa(john, human)” is proved. 36
- 37. Elaborated Example We illustrate the forward reasoning and backward reasoning in Extended Semantic Networks using the following information. – Every man is a human. – Every human is animate. – Every animate thing is a living thing. – John is a man. This can be represented in FOL as clauses human(X) man(X) animate(X) human(X) living_thing(X) animate(X) man(john) 37
- 38. Elaborated Example - ESNet 38 isa(X, living_thing) isa(X, animate) isa(X, animate) isa(X, human) isa(X, human) isa(X, man) isa(john, man) living_thing X animate john man isa isa isa isa isa isa X1 X2 human isa
- 39. Forward Reasoning in Esnet 39 living_thing X animate john man isa isa isa isa isa isa X1 X2 human isa X2 = John
- 40. Forward Reasoning in Esnet 40 living_thing X animate isa isa isa isa X1 john human isa X1 = John
- 41. Forward Reasoning in Esnet 41 living_thing X animate john isa isa isa X = John john isa living_thing
- 42. Backward Reasoning (textbook) 42 living_thing animate john man isa isa isa isa isa isa X1 X2 human isa denial X2 = John X
- 44. Backward Reasoning … 44 living_thing animate isa isa isa X = John X john john living_thing isa isa Contradiction!!
- 45. Backward Reasoning – a better way 45 living_thing animate john man isa isa isa isa isa isa X1 X2 human isa denial X = John X
- 46. Backward Reasoning … 46 animate john man isa isa isa isa isa X1 X2 human X1 = John
- 47. Backward Reasoning … 47 john man isa isa isa X2 human X2 = John
- 48. Backward Reasoning … 48 john man isa isa Contradiction!!
- 49. Elaborated Example 2 John gives an apple to Mike. John likes an apple. Anyone who gives away anything he likes must like the person he gives it to. Represented in FOL as clauses: action(e, give) actor(e, john) recipient(e, mike) object(e, apple) likes(john, apple) likes(X, Z) action(E, give), actor(E, X), recipient(E, Z), object(E, Y), likes(X, Y) 49
- 50. Elaborated Example 2 – forward reasoning 50 X = john Y = apple likes likes give likes E e apple john mike Y X Z
- 51. Elaborated Example 2 – forward reasoning 51 E = e Z = mike give E e apple john mike Z
- 52. Forward Reasoning in Esnet 52 john mike E = e; Z = mike likes
- 53. Backward Reasoning in Esnet 53 likes likes give likes E e apple john mike Y X Z likes X = john Z = mike
- 54. Backward Reasoning in Esnet 54 likes give E e apple john mike Y Y = apple
- 55. Backward Reasoning in Esnet 55 give E e apple john mike For E = e, we get an empty clause.
- 56. Inheritance in ESnets 56 living_thing X animate john man isa isa isa isa isa isa X1 X2 human isa part_of two_legs isa(X, living_thing) isa(X, animate) isa(X, animate) isa(X, human) isa(X, human) isa(X, man) isa(john, man) part_of(two_legs, human)
- 57. Inheritance in ESnets 57 living_thing X animate john man isa isa isa isa isa isa X1 X2 human isa part_of two_legs denial part_of X2 = john
- 58. Inheritance in ESnets 58 living_thing X animate john isa isa isa isa X1 human isa part_of two_legs part_of contradiction
- 59. Ch 7 Knowledge Representation Semantic Networks – Graphical representation (easy to visualise) – In-built inheritance – Easy to add new information – Cannot represent • Negation • Disjunction Extended Semantic Networks – Extend semantic networks with clauses to get best of both semantic networks and logic (handling negation and disjunction). 59
- 60. Ch 7 homework Draw a semantic network representing: Every vehicle is a physical object. Every car is a vehicle. Every car has four wheels. Electrical system is a part of car. Battery is a part of electrical system. Pollution system is a part of every vehicle. Vehicle is used for transportation. Suzuki is a car. Every living thing needs oxygen to live. Every human is a living thing. John is human. – Answer the query whether John is a living thing and john needs oxygen to live using inheritance. 60
- 61. Ch 7 homework Draw an extended semantic network representing: Teachers who works hard are liked by students. May is a hard working teacher. John is a student. Therefore, John likes Mary. Everyone who sees a movie in a theatre has to buy a ticket. A person who does not have money cannot buy a ticket. John sees a movie. Therefore, John had money. All senior citizens and politicians get air concession. Mary is a senior citizen. John does not get air concession. Therefore, May gets air concession and John is neither senior citizen nor politician. Every member of ROA is either retired or an officer. John is a member of ROA. Therefore, John is either retired or an officer. 61