1. Knowledge Engineering

2. Contents Knowledge Representation Rule-Based Representation
Frame-Based Representation
Semantic-Networks

3. What is frame?
A frame is a data structure with typical knowledge about a particular object or concept.
Followings are two typical frames with knowledge about airline passengers. Both frames have the same structure.
Each frame has its own name & a set of attributes or slots, associated with it.
QUANTAS BOARDING PASS
Carrier: QUANTAS AIRWAYS
Name: MR N BLACK
Flight: QF 612
Data: 29DEC
Seat: 23A
From: HOBART
TO: MELBOURNE
Boarding:0620
Gate: 2
AIR NEW ZEALAND BOARDING PASS
Carrier: AIR NEW ZEALAND
Name: MRS J WHITE
Flight: NZ 0198
Data: 23NOV
Seat: 27K
From: MELBOURNE
TO: CHRISTCHURCH
Boarding:1815
Gate: 4
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4. Frames as knowledge representation technique
The concept of a frame is defined by a collection of slots or attributes.
Each slot describes a particular attribute or operation of the frame.
Slots are used to store values.
A slot may contain a default value or a pointer to another frame, a set of rules or procedure by which the slot value is obtained.
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5. Frame-Based Knowledge Representation
How are objects related in a frame-based system?
There are 3 types of relationships between objects:
Generalization:
It denotes “a-kind-of” or “is-a” relationship between super-class and its sub-class.
For example, a car is-a vehicle, or in other words, Car represents a subclass of the more general super-class Vehicle.
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6. Frame-Based Knowledge Representation
Generalization
CLASS: Vehicle
is-a
is-a
is-a
CLASS: Car
CLASS: Airplane
CLASS: Boat
Superclass: Vehicle
Superclass: Vehicle
Superclass: Vehicle
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7. Frame-Based Knowledge Representation
How are objects related in a frame-based system?
Aggregation:
It is “a-part-of” or “part-whole” relationship in which several subclass representing components are associated with a super-class representing components are associated with a super-class representing a whole.
For example, an engine is a part of a car.
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8. Frame-Based Knowledge Representation
Aggregation
CLASS: Car
a-part-of
a-part-of
a-part-of
CLASS:Engine
CLASS:Transmission
CLASS:Chassis
Superclass:Car
Superclass:Car
Superclass: Car
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9. Frame-Based Knowledge Representation
How are objects related in a frame-based system?
Association:
It describes some semantic relationship between different classes which are unrelated otherwise.
For example, Mr. Black owns a car.
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10. Frame-Based Knowledge Representation
Association
CLASS: Mr. Black
owns
owns
owns
Belongs-to
Belongs-to
Belongs-to
CLASS: Car
CLASS: Computer
CLASS:House
Superclass: Mr. Black
Superclass:Mr. Black
Superclass: Mr. Black
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11. Frame-Based Knowledge Representation
Advantages
We can define the given problem in abstract way.
Frames provide a way for the structured and concise representation of knowledge.
In a single entity, a frame combines all necessary knowledge about a particular object or concept.
During a search for a specific item, we go directly to the item’s instance frame that contains the desired goal.
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12. Frame-Based Knowledge Representation
Disadvantages
Idea behind the frame based system is easy, but implementation is difficult.
It can not distinguish between essential properties and accidental properties of a frame.
Hence, in a complex case, it is difficult to predict how these features will interact, or to explain unexpected interactions, which makes debugging and updating difficult.
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13. Semantic Network
SN was first proposed by Quillian in 1966, as a model of human memory
Semantic network (SN) is a graph-based representation
It is a directed graph
A SN is a network of nodes and links to represent the definition of a concept (or a collection of concepts)
The nodes represent concepts
The links represent the relations between concepts
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14. Semantic Network
In these networks, objects are shown by nodes, and links between the nodes describe
the relationship between two objects, for example,
Mary is an instance of trainer, and trainer is a type of consultant.
A trainer trains a programmer and a programmer is an employee.
Joe is an instance of programmer.
From this we can clearly see the relationship that may exist between Mary and Joe.
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15. Example
Draw a diagram representing the relationships between Mary and Joe, indicating the relationship between a trainer, consultant, programmer and employee.
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16. Example
Such a diagram is the beginning of a semantic network but this can be improved by more closely defining the nature of the relationships.
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17. Inheritance
Inheritance is concerned with how one object inherits the properties of another object.
In the diagram you created in the previous activities, identify from which classes Mary and Joe inherit properties.
You should have been able to recognize that Mary, in being a trainer, inherits the properties of the consultant class and that Joe, in being a programmer, inherits the properties of the employee class.
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18. Inheritance
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19. Inheritance
You should have been able to recognize that from this semantic network it would be possible to conclude that the grass snake Slither is a vegetarian and Slither eats meat. Clearly, these conclusions are contradictory. Which conclusion we reach depends where in the network we start and which links we follow. This process is unreliable.
Thus, to perform inference using a semantic network you must understand the meaning of the links and follow the correct links. As the links can be many, and varied, performing inference using a semantic network is complex and unreliable.
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20. Example
Mammal is a kind of animal that has vertebrata. Cat, Bear and whale are mammal. Cat and Bear has fur. Fish is a type of animal. Whale is a Fish
and Fish lives in water.
Vertebrata
Cat
Fur
Animal
Mammal
Bear
Fish
Water
Whale
is-an
is-a
Lives-in
Is-a
has
Has-a
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21. Advantages Explicit in visualization and easy to understand
Often used as a communication tool between the knowledge engineer and the expert during the knowledge acquisition phase
SNs are particularly good at representing knowledge in the form of hierarchies
Knowledge is hierarchically categorized (classified)
Quick inference possible
Supports default reasoning in finite time
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22. Disadvantages No interpretation standard – Lack well-defined semantics
They are less reliable than other knowledge representation techniques because inferring becomes a process of searching across the diagram.
Quite limited inference possible
Diagrams can become very complex.
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