Understanding Knowledge Representation in Information Systems

Lecture : 6 Knowledge Representation, Approaches to representations

The Knowledge : Knowledge is a general term that requires an answer to this question: how to represent knowledge? The answer to this question requires: An analysis of the differentiation between knowledge "how" and "knowledge" that "or what. . " " " " knowing "how to do something Such as: knowing how to drive a car, and this is called procedural knowledge .

knowing "that something is trueor false Such as: knowing the speed limits of a car on the highway, and this is called declarative knowledge . . : The knowledge and the way knowledge is represented are quitedifferentconcepts: They play two central but different roles in smart systems. .

This is what happens in the real world . But in the case of computer, we want it to think the language of realityand mind.

Knowledge is : a General term and determining the system's competence based on what the system knows. . But knowledge representation is: the method of coding knowledge, so that define the system's performance in doing some things . .

Each different type of knowledge requires a different kind of representation of knowledge Knowledge Representation The representation of knowledge is to make the computer do what the human mind wants so that it thinks as a human thinks, takes information and analyzes it like human. .

Definition of knowledge: As we have said, knowledge is a general term, also define as a sequence that begins with Limited utilitydata . : . By organizing and analyzing these data we arrive at what it means, and we have information. . By interpreting and evaluating that information, we gain knowledge. .

By understanding the principles contained in this knowledge we reach wisdom . .

Knowledge Progression : The figure below illustrates the previous definition of the sequence of knowledge from data to wisdom, which is done with human in a manner natural, and the artificial intelligence aims to complete. .

The data : The data is seen as a set of facts separate from each other, forexample, we say: it is raining. . : The information : The information appears when the relationships between the facts are understood , as a answer to questions: who, what, where and when .

for example, when we link the facts in the previous example, we say: The temperaturedropped to 15 , making it rain . : . The Knowledge : Knowledge emerges when we define and understand patterns between previous relationships. we get answers to the question how ? .

for example, when we say: If the humidity is very high and the temperature is actually low . So it rained. : . The Wisdom: Wisdom is the height of understanding, and by revealing the foundations of relationships that describe patterns, we will get theanswer to thequestion why?

Knowledge categories : Knowledge is classified into two types: : 1- Implicit knowledge : 2- Explicit knowledge (1) Implicit knowledge : indicates the types of informal knowledge that are not clear . ) ( (2)Explicit knowledge : indicates the formal types of knowledge .

Differences between implicit and explicit knowledge: Implicit knowledge Explicit knowledge 1. Exists within a human being; it is 1. Exists outside a human being; it is embodied. embedded. . . 2. We can put it in a formal way 2. Difficult to formalize. . 3. Can be shared, copied, processed 3. Difficult to communicate or share and stored . 4. Hard to steal or copy 4. Easy to steal or copy . 5. Drawn from experience, action 5. Drawn from formal documentation, procedures, processesand concepts subjective insight

Knowledge Types 1-Procedural knowledge -Is knowledge about how to do things, -Focus on actions that must be accomplished to reach a particulargoal or partial goal. Examples: Procedures, rules, agendas, models .

: 2-Declarative knowledge -Knowing that something is rightorwrong -Focus on the representation or presentation of objects and events, and knowledge about facts and relationships . Examples: Concepts, objects, facts, propositions, assertions, semantics, logic, and description models. : .

Knowledge Representation Schemes: There are four types of representation of knowledge: 1- Relational Knowledge 2- Inheritable Knowledge 3- Inferential Knowledge 4- Declarative Knowledge

: (1) Relational Knowledge This knowledge binds one domain to another, relational knowledge consists of objects that contain properties and their values. The results of this type of knowledge transform elements through differentareas . .

(2) Inheritable Knowledge: Here knowledge represents elements that inherit properties from objects that are parents . . Property inheritance : Means that the object or element inherits from a particular class type, attributes and values from the more general categories, where the classes are hierarchically arranged. .

(3)Inferential Knowledge : This knowledge produces new information from the given information, and the new information does not require the collection of additional data from the source, but requires analysis of information given togenerate new knowledge. .

(4) Declarative knowledge Here, knowledge is a process of transformation between areas : that define "what to do when ... and the representation of "how todo it ... .. " " ... " "

Knowledge Representation Techniques Models of representation of knowledge and its mechanisms are usually based on : 1. Logic 2. Rules 3. Frames 4. Semantic Networks

(1) Logic: Logic is one of the oldest ways to represent knowledge. The representation of knowledge and the building of its rules requires the conversion of these sentences into formulas that are easy to represent within the computer systems . .

And sentences according to the propositional Logic, either be simple sentences orcompound. . (2) Rules : Rules: A knowledge structure that links some information with other ones , which can be inferred or to infer it from the facts . . :

And be on the formula: IF condition THEN action statements Example IF Temperature is hot THEN turn on the air-conditioning system .) ( ) ( Rules Based System : Rule 1:IF the ball s color is red THEN I like the ball. Rule 2:IF I like the ball THEN I will buy the ball.

(3) Frames It is one way to represent knowledge in expert systems and is a special way of writing knowledge data in the form of a general structure that contains frames. . This structure can contain general properties of general things : Example. The bird classification framework refers to general properties of the bird. .

Or to describe the unique properties of the classification frame. Example: Ostrich class of a bird class framework : .

(4)Semantic Networks Is one of the methods used to represent knowledge in expert systems , It is the representation of knowledge in the form of network's structure . .. . Thesemantic network consists of: 1- A set of objects called nodes 2. A set of links, connect between nodes

Exceptional handling : Some Exceptions to Certain Situations "All birds are able to flyand ostrich birds" Can the ostrich fly? The Exampleof Semantic Networks (Bird) Fact : Parrot is a bird. Typically bird has wings and travel by flying. Bird category falls under animal kingdom. All animal requires air to breathe. Ostrich is a bird but travels by walk. . . . . : .