My question was more to the different methodology of knowledge
Representations (KR) and Knowledge Base (KB) types of designs and their
performance at retrieving facts in respect to the computer time/computer
instructions required to retrieve facts and storage requirements.
Well, viewing the memory problem as "retrieving facts" is in itself a
serious philosophical statement ...
Storing crisp, declarative facts efficiently is not *such* a hard
problem; one can use for instance a hypergraph data structure, with
multiple indices constructed to make frequent queries rapid. One can
even automate the construction of new indices. The space/time
tradeoff rears its head here in that more indices means faster access
but more memory usage.
The subtler conceptual issue, IMO, regards how to store uncertain,
context-dependent patterns of knowledge: these may be stored in the
same manner as crisp declarative facts, or in a thoroughly distributed
way as in an Attractor Neural Net, or via some combination approach...
-- Ben
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