What is a knowledge representation? We argue that the notion can best be understood in terms of five distinct roles it plays, each crucial to the task at hand:
• A knowledge representation (KR) is most fundamentally a surrogate, a substitute for the thing itself, used to enable an entity to determine consequences by thinking rather than acting, i.e., by reasoning about the world rather than taking action in it.
• It is a set of ontological commitments, i.e., an answer to the question: In what terms should I think about the world?
• It is a fragmentary theory of intelligent reasoning, expressed in terms of three components: (i) the representation’s fundamental conception of intelligent reasoning; (ii) the set of inferences the representation sanctions; and (iii) the set of inferences it recommends.
• It is a medium for pragmatically efficient computation, i.e., the computational environment in which thinking is accomplished. One contribution to this pragmatic efficiency is supplied by the guidance a representation provides for organizing information so as to facilitate making the recommended inferences.
• It is a medium of human expression, i.e., a language in which we say things about the world.

Knowledge representation is needed for library classification and for processing concepts in an information system. In the field of artificial intelligence, problem solving can be simplified by an appropriate choice of knowledge representation. Representing the knowledge in one way may make the solution simple, while an unfortunate choice of representation may make the solution difficult or obscure; the analogy is to make computations in Hindu-Arabic numerals or in Roman numerals; long division is simpler in one and harder in the other. Likewise, there is no representation that can serve all purposes or make every problem equally approachable.