Interesting piece on topic maps, KR and Cyc by Eliot Kimber:
Thus my conclusion that topic maps, by themselves, do not in any really meaningful way “capture knowlege”. They can at best provide identifying objects for concepts, express simple facts about those concepts in relation to each other, and bind those facts to instances of the concepts. But that’s it. This is information. Very useful information and a sophisticated way to capture it, but it is not knowledge.
I think the same applies to RDF, with and without OWL. Or not?
We are lacking a reasonable definition of the terms “knowledge” and “information” here.
“Knowledge” as used in KR.
“Information” as in “data with enough context to figure out what the data is about.”
Out of my depth here, but CYC does lurk in the history of RDF, albeit indirectly. The most obvious missing component is contexts, which are still present in a way (i.e. N3).
I guess as RDF stands now the topic map problem carries over, however there seems to have been more awareness of the microtheory-type issues in RDF. The web does present many idiosyncratic, contradictory, ideas about the world (often down right cranky :-) and the named graph work, and more simply sparql, provide some means to deal with them.
I think Maggi is right, and that your Wikipedia reference does not help.
Eliot clarifies his point in a comment to his own posting: Topic Maps and/or RDF by themselves don’t take you very far if you want to build something like Cyc. Which is perfectly true, but I’m not sure it’s a very interesting conclusion. Who wants to build something like Cyc, anyway?
As for OWL it does allow some measure of reasoning using Description Logic, but that’s not context-aware, nor anything like as powerful as what CycL (the technology underlying Cyc) allows.