Back in the 60′s and 70′s of the last century, the Whorfian hypothesis was a hot subject on college campuses. This was the idea that one’s native language, its syntax and semantics, strongly shaped one’s worldview. For example, Eskimos speaking Inuit supposedly had thirty different words for snow and so had a more complex relationship with their environment than someone speaking English with only one word for snow.
The problem of course is that skiers can make plenty of distinctions about kinds of snow even in English. Despite Whorfian hypothesis being theoretically attractive, it did not square in the end with our actual experience with language. That pretty much took the steam out of the Whorfian hypothesis, but now in the 21st Century, empirical support has been accumulating for a weaker version of it. This was the subject of an article in New York Times Magazine (http://nyti.ms/boqzs5).
The weak Whorfian hypothesis rejects the idea that language establishes an absolute limit on thinking. Thus we can learn about distinctions in types of snow if we really need them. The structure of a language, however, definitely can bias our thinking; and this could have consequences in practical matters like the ranking of retrieved documents. The choice of a particular semantic framework like RDF may therefore affect the performance of an information system in unexpected ways.
So far, experimental results on language and thought have focused on highly specific biases in areas of language like giving spatial directions, assigning gender to nouns, and dividing the spectrum into colors. It seems plausible, though, that this should generalize to the overall semantic problem of dividing up meaning into some kind of compact space. There is more than one way to skin a cat here, and there are probably advantages and disadvantages in each possibility.
A dogmatist might be tempted to argue here that RDF with certain standard taxonomies is the right way and everything else is wrong, but that is probably overreaching. We are not yet savvy enough about semantics to carve tablets in stone about its implementation. At present, one can say only whether a given scheme is optimal in some formal sense; but if it makes no obvious sense to people, then something more comprehendable might be better in the long run even if it is less than optimal.
The weak Whorfian hypothesis forces us to be more honest. If each semantic scheme introduces its own biases, then we need to experiment to see how different approaches work out for a given target application. Given that humans operate with more than one linguistic framework, we should not be so quick to assume than machines can do better at semantics with just a single framework.