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Ontological Versus Knowledge Engineering
March 1989 (vol. 1 no. 1)
pp. 84-88

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.

[1] D. Lenat and E. A. Feigenbaum, "On the thresholds of knowledge," inProc. IJCAI-87, Milan, Italy, 1987; Also in MCC Tech. Rep. AI- 126-87, May 1987.
[2] D. Lenat and R.V. Guha,Building Large Knowledge-Based Systems: Representation and Inference in the Cyc Project, Addison-Wesley, Reading, Mass., 1990.
[3] M. Minsky,Society of Mind. New York: Simon&Schuster, 1985.
[4] B. Russell,Human Knowledge, It's Scope and Limitations. New York: Unwin, 1948.

Index Terms:
knowledge engineering; knowledge base; database; DB; KB; intelligent systems; brittleness; unforeseen problems; representation trap; multiple expert systems; rules; ontological engineering; database management systems; knowledge engineering
Citation:
D.B. Lenat, "Ontological Versus Knowledge Engineering," IEEE Transactions on Knowledge and Data Engineering, vol. 1, no. 1, pp. 84-88, March 1989, doi:10.1109/69.43405
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