What is Knowledge Representation?

· Artificial intelligence is a system that is concerned with the study of understanding, designing and implementing the ways,  associated with knowledge representation to computers.

· In any intelligent system, representing the knowledge is supposed to be an important technique to encode the knowledge.

· The main objective of AI system is to design the programs that provide information to the computer, which can be helpful to interact with humans and  solve problems in various fields which require human intelligence.

What is Knowledge?

· Knowledge is an useful term to judge the understanding of an individual on a given subject.

· In intelligent systems, domain is the main focused subject area. So, the system specifically focuses on acquiring the domain knowledge.

Types of knowledge in AI

Depending on the type of functionality, the knowledge in AI is categorized as: 
1. Declarative knowledge

· The knowledge which is based on concepts, facts and objects, is termed as 'Declarative Knowledge'.

· It provides all the necessary information about the problem in terms of  simple statements, either true or false.

2. Procedural knowledge

· Procedural knowledge derives the information on the basis of rules, strategies, agendas and procedure.

· It describes how a problem can be solved.

· Procedural knowledge directs the steps on how to perform something.
For example: Computer program.

3. Heuristic knowledge

· Heuristic knowledge is based on thumb rule.

· It provides the information based on a thumb rule, which is useful in guiding the reasoning process.

· In this type, the knowledge representation is based on the strategies to solve the problems through the experience of past problems, compiled by an expert. Hence, it is also known as Shallow knowledge.

4. Meta-knowledge

· This type gives an idea about the other types of knowledge that are suitable for solving problem.

· Meta-knowledge is helpful in enhancing the efficiency of problem solving through proper reasoning process.

5. Structural knowledge

· Structural knowledge is associated with the information based on rules, sets, concepts and relationships.

· It provides the information necessary for developing the knowledge structures and overall mental model of the problem.

The main objective of knowledge representation is to draw the conclusions from the knowledge, but there are many issues associated with the use of knowledge representation techniques. 

Some of them are listed below:



Refer to the above diagram to refer to the following issues.

1. Important attributes

There are two attributes shown in the diagram, instance and isa. Since these attributes support property of inheritance, they are of prime importance.

2. Relationships among attributes

Basically, the attributes used to describe objects are nothing but the entities. However, the attributes of an object do not depend on the encoded specific knowledge. 

3. Choosing the granularity of representation

While deciding the granularity of representation, it is necessary to know the following:

i. What are the primitives and at what level should the knowledge be represented?

ii. What should be the number (small or large) of low-level primitives or high-level facts?

High-level facts may be insufficient to draw the conclusion while Low-level primitives may require a lot of storage.
For example: Suppose that we are interested in following facts: 
John spotted Alex. 

Now, this could be represented as "Spotted (agent(John), object (Alex))"

Such a representation can make it easy to answer questions such as: Who spotted Alex? 

Suppose we want to know : "Did John see Sue?" 
Given only one fact, user cannot discover that answer.

Hence, the user can add other facts, such as "Spotted (x, y) → saw (x, y)"