In the nature of the enterprise, KR as it has been practised in AI has developed strong affinities with disciplines such as philosophy and linguistics, where the nature of what exists, what we can know about it, and how that knowledge is represented are of key importance.
These relationships are sometimes regarded as surprising by those whose expectations are that a computer-based subject should be more naturally allied with engineering and the natural sciences.
This question can be approached in many different ways, but one can broadly distinguish between approaches which seek to discover, and thereby emulate, the forms in which knowledge is represented in the human brain, and those which take their inspiration from the forms of representation used by humans to encode their knowledge, notably language, mathematics, and formal logic.
The term Knowledge Representation, when used in the AI context, is generally taken to refer to approaches of the latter kind rather than the former, which are regarded as more within the province of Cognitive Science.
Another emphasis of KR has been on what is known as ‘commonsense knowledge’; this is the kind of knowledge that we humans routinely deploy in our day-do-day existence when we are not engaged in tasks that require technical knowledge and skills that have been acquired through specialist training (Hobbs and Moore ).
Knowledge Representation (KR) originated as a subfield of Artificial Intelligence (AI).
In the early days of AI, it was sometimes imagined that to endow a computer with intelligence it would be sufficient to give it a capacity for pure reasoning; it quickly became apparent, however, that the exercise of intelligence inevitably involves interaction with an external world, and such interaction cannot take place without some kind of knowledge of that world.
Before turning to a review of these techniques, however, a few more general remarks are in order.
It should be emphasised that the term ‘knowledge’ implies much more than just facts, information, or data.