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Knowledge Engineering

Knowledge Engineering. Contents. Knowledge Representation Rule-Based Representation Frame-Based Representation Semantic-Networks. What is frame? A frame is a data structure with typical knowledge about a particular object or concept.

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Knowledge Engineering

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  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. 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 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

  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.

  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.

  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

  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.

  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

  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.

  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

  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.

  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.

  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

  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.

  15. Example • Draw a diagram representing the relationships between Mary and Joe, indicating the relationship between a trainer, consultant, programmer and employee.

  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.

  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.

  18. Inheritance

  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.

  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. has Vertebrata Cat Fur is-a has Has-a Bear Mammal Animal is-an is-a is-a is-an Is-a Whale Lives-in Fish Water

  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

  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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