I tested OpenCYC three years ago while working for a startup that did semantic tagging & recommendation of text-based content.
Essentially, we would take something like:
Gov. Rick Perry has said he will no longer wear cowboy boots, which some believe is part of an attempt to soften his gunslinging image as he considers another run for president.
And map it to something more machine-readable:
"Gov." -> http://dbpedia.org/page/State_(polity) (10% confidence)[1]
"Rick Perry" -> http://dbpedia.org/page/Rick_Perry (95% confidence)
"Rick Perry" -> http://sw.opencyc.org/concept/Mx4rM7N6iOeUSpGar4HaqXF3zg (95% confidence)
"Cowboy boots" -> http://dbpedia.org/page/Cowboy_boot (75% confidence)[2]
"President" ->http://dbpedia.org/page/President (90% confidence)[3]
Let's dive into what you get from OpenCyc vs DBpedia (ontology sourced from Wikipedia).
DBpedia:
Tons of machine-readable information like Party, Alma Mater, birthday, spouse, etc. Extensive categorical links (dcterms:subject) like category:United_States_presidential_candidates,_2012.
OpenCyc:
Knows he's a politician affiliated with "Republican Party," "Democratic Party," "The Republican Party," and "The Democratic Party"
I highlight only one example, but this is all over the place. Opencyc has duplicate terms where dbpedia/wikipedia does not. Opencyc has far less information. Opencyc has more incorrect information.
This is inevitable when you consider the two approaches. Wikipedia has tens of thousands of people making connections and updating the resource, where opencyc relies more on scripts. Opencyc, and quite possibly Cyc, is already antiquated by Wikipedia.
[1] Note that the confidence was estimated from heuristics I wrote based on how the ontologies were put together
[2] OpenCyc typically does not have mappings for plurals, while wikipedia has a very convenient redirect system for string mappings
[3] OpenCyc matches President with 60 concepts. Too much noise to do anything with