Properties for knowledge Representation

Properties for knowledge Representation

The following properties should be possessed by a knowledge representation system.

a.     Representational Adequacy: It is the ability to represent the required knowledge.

b.    Inferential Adequacy: It is the ability to manipulate the knowledge represented to produce new knowledge corresponding to that inferred from the original.

c.     Inferential Efficiency: The ability to direct the inferential mechanisms into the most productive directions by storing appropriate guides.

d.    Acquisitional Efficiency: The ability to acquire new knowledge using automatic methods wherever possible rather than reliance on human intervention.

KNOWLEDGE REPRESENTATION

Knowledge representation is probably, the most important ingredient for developing an AI. A representation is a layer between information accessible from outside world and high level thinking processes. Without knowledge representation it is impossible to identify what thinking processes are, mainly because representation itself is a substratum for a thought.

The subject of knowledge representation has been messaged for a couple of decades already. For many applications, specific domain knowledge is required. Instead of coding such knowledge into a system in a way that it can never be changed (hidden in the overall implementation), more flexible ways of representing knowledge and reasoning about it have been developed in the last 10 years.