What you suggest, using previously acquired knowledge to learn new concepts, is par for the course in my field of study, Inductive Logic Programming. ILP is a set of algorithms and techniquest that learn logic programs from examples and background knowledge, where the examples and BK are themselves logic programs.
Initially the BK comes from some existing source- it can be a hand-crafted database of a few predicates deemed relevant to the learning task or a large, automatically-acquired database mined from some text source, data from the CYC Project of course, etc. In any case, because of the unified representation, learned hypotheses (the "models") can be used immediately as background knowledge to learn new concepts.
Edit: I don't know if the Cyc project uses ILP. But what the comment above says is doable.
Initially the BK comes from some existing source- it can be a hand-crafted database of a few predicates deemed relevant to the learning task or a large, automatically-acquired database mined from some text source, data from the CYC Project of course, etc. In any case, because of the unified representation, learned hypotheses (the "models") can be used immediately as background knowledge to learn new concepts.
Edit: I don't know if the Cyc project uses ILP. But what the comment above says is doable.