3203. Introduction to Artificial Intelligence

Knowledge Representation


1. Knowledge

In general, AI systems increasingly depend on domain knowledge, and this tendency is being strengthened by the availability of more knowledge.

There are several dimensions by which knowledge can be classified into types:

All these types of knowledge need to be handled in AI, so they must be first represented in AI systems, so as to be processed. Requirements for knowledge representation includes expressiveness, modularity, naturalness, flexibility, and efficiency. Usually, tradeoff among the factors needs to be considered.

There is always a relationship between the form in which knowledge is represented and the way in which the knowledge is used. The study in knowledge representation has been influenced by several disciplines:

The relationships among different representations are important. For example, here are some tips on translating from English into first-order predicate logic.


2. Structure of knowledge

Though in principle is is possible to see all the knowledge of a system as a set of propositions, it is not a good idea for implementation. For efficiency considerations, it is necessary to organize knowledge into larger structures. Here are some attempts: Here are some examples where these structures are implemented using Prolog: One conclusion reached by many researchers in this study is that certain conceptual relations play important roles in knowledge, and therefore deserve special treatment. In particular, the relation between a concept and its subconcept forms the concept hierarchy that is crucial in problem solving. This relation is often called the "is-a" relation, and is used with a special inference rule that realize property inheritance.

Description Logic is an attempt to combine some of the above ideas with first-order predicate logic by balancing expressing power and inference efficiency. It is more expressive than propositional logic but has more efficient decision problems than first-order predicate logic. It can also describe concept hierarchies.


3. Knowledge base

Usually knowledge in a certain domain or for a certain purpose is collected together in a knowledge base. Beside providing retrieval capacity, the knowledge base is often equipped with an inference engine to generate derived knowledge. Also, the consistency and integrity of knowledge need to be maintained.

Representative knowledge bases in AI:

With the coming and growing of the Internet, structured online data are also widely used in AI systems: