Abstract
The author first discusses the difference between a knowledge base (KB) and a database (DB), which seems to hinge on the 'gray box' verses 'black box' nature of the entries. He then discusses the need for a huge KB to break today's bottleneck in intelligent systems, i.e. their brittleness when confronted by unforeseen problems. That same brittleness-the representation trap-is what prevents multiple expert systems from cooperating or even sharing rules. The author then considers the central question of the present work: How is the task of building a huge KB different from that of building n small KBs? It is shown that this leads into the realm of ontological engineering, and it is found that there is no single, elegant 'use-neutral' solution to the problem, at least not at present, but that a kind of variegated 'tool-box' approach might succeed.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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