Approaches to Knowledge Representation

Approaches to Knowledge Representation

A good knowledge representation system should have the following approach to representing the given information so that the reasoning becomes easy.

1. Representational Adequacy: It is the ability to represent all kinds of knowledge that are needed in a particular domain. This means a good knowledge representation should be able to represent any kind of knowledge in a standard manner.
   
   
2. Inferential Adequacy: It is the ability to manipulate the different facts that are represented in a standard format in such a way that it derives new structured knowledge from an old one. In short, a good knowledge representation system should be able to infer the new facts from the given facts.
   
   
3. Inferential Efficiency: It is the ability to derive the new facts from the given fact in an efficient or optimal manner that is a good knowledge representation should be able to incorporate some additional information which can be used to focus the attention of inference engine in the most promising direction.
   
   
4. Acquisitional Adequacy: It is the ability to acquire new knowledge from the environment in an efficient manner.

Types of Knowledge

1. Simple Relational Knowledge:It is the simplest way of storing facts by using a relational method where each fact about a set of objects is set out systematically in columns. This representation gives little opportunity for inference but it can be used as the knowledge basis for inference engines.
   A simple way to store facts.
   Each fact about a set of objects is set out systematically in columns.
   Little opportunity for inference.
   Knowledge basis for inference engines.
   
   
2. Inheritable Knowledge: It is a referential knowledge which is made up of objects that consist of:
   Attributes
   Corresponding associated values.
   
   
3. Inferential Knowledge: It is a representation knowledge as formal logic.
   

   For example: All dogs have tails.
   ∀x: dog(x) → has a tail(x).
   Advantages:
   It sets certain rules which are very strict which can be used to derive more facts. The truth of the new statement can be verified.
   It guarantees the correctness.
   Many inference procedures available to in implement standard rules of logic.
   Popular in AI systems. e.g Automated theorem proving.

4. Procedural Knowledge: The basic idea of procedural knowledge is to encode the knowledge in some procedures. These procedures may include small programs that know how to do specific things and how to proceed.

References:

1. Elaine Rich, Kevin Knight 1991, "Artificial Intelligence".
2. Nilsson, Nils J. Principles of Artificial Intelligence, Narosa Publishing House New Delhi, 1998.
3. Norvig, Peter & Russel, Stuart Artificial Intelligence: A modern Approach, Prentice Hall, NJ, 1995
4. Patterson, Dan W. Introduction to Artificial Intelligence and Expert Systems, Prentice Hall of India Private Limited New Delhi, 1998.