Diffrent types of Approach

Data Science, AI and ML
#1

Different approaches are used by knowledge representation system. Those are

  1. Simple relational knowledge

This knowledge is used to store data systematically and in the form of columns. The only thing to know is they contain relation with each other and they very little chances to make an inference which can be later used in inference engines.

Name Style Instrument Age
John Jazz Trumpet 35
Prince Rock Guitar 40
Monty Rock Trumpet 45

The above table can give answers to

  • Who plays in rock style?
  • Who plays trumpet in rock style?
  1. Inheritable knowledge

This type of knowledge can be passed on other agents without having a need of learning again. If an AI agent learns something from a human, then it can pass it to other agents and they can inherit the same without learning again.

This type of knowledge is generally obtained from associated objects and tries to prescribe a new structure which extracts all or selective attributes from existing objects. This type of knowledge is indulged in the design hierarchies which is found in physical, functional and process domains. So the parent attributes try to inherit the knowledge within the hierarchy to prescribe to the child elements.

3**. Inferential knowledge**

It defines the knowledge as a formal logic condition and has a strict rule. The knowledge is extracted from objects by studying the relation between them. If we take a word to make an inference, it will difficult except we take a phrase to get more meaningful insights of that same word. In linguistic, this approach is known as semantics. The new information extracted from the existing information does not require gathering of data from the source but they analyze the existing information in order to generate new knowledge.

  1. Procedural knowledge

This knowledge tends to represent control information which uses the knowledge keeps embedded in the knowledge itself. This approach can easily represent heuristic or domain specific knowledge. They are represented as small programs of how to proceed and perform specific things. They may include inferential efficiency but they do not have inferential adequacy or acquisitional efficiency.

Knowledge representation theory is suitable when intelligent behavior solely depends on explicitly represented knowledge. Knowledge representation is not capable to solve anything by itself if a system fails to reason what it has represented explicitly in the mist effective way. Knowledge representation is a study of the information we can extract in a computationally dependable way or investigating the area within the theories of KR hypothesis. If a theory consumes classical first order logic assumptions, then knowledge representation is the basis of this investigation or else it is recommended to explore other theories.