Once you start to sit down and try and encode all knowledge in a formal manner, one begins running into Godelian imcompleteness. Tugging on the string and trying to resolve inconsistencies leads to more special cases and more descriptions, which in turn lead to more special cases and more descriptions, and so on. In "Ambient Findability", a book on the future of information finding, author Peter Morville calls Knowledge Representation the "quagmire" of good old-fashioned AI.
There is a certain benefit to the formal representations used by the semantic web. Namely, that communication between different systems is enabled by communicating using discrete tokens. The discreteness of these tokens make the choice of token unambiguous, but does not mean that it is a perfect map of meaning. This is similar to language, which is an incomplete and imperfect representation of what is nebuluous and informal in the brain. For example, if I use the word "healthy", you can be pretty confident that I used this particular word (assuming you heard me okay), but you are not necessarily confident that you know precisely what that actually means. We communicate with distinct tokens as our language not because it is a perfect map of meaning, but because its better than using continuous linguistic representations. Imagine if you weren't even sure what word I used at any given point in time, because words are fluidly flowed into each other. So even though knowledge is better modeled probabilistically and continuously, it might be difficult to communicate with a language whose representations was explicitly probabilistic and continuous.
The semantic web shouldn't be viewed as a be-all end-all perfect encoding of knowledge. Rather, it should be viewed as a language for machines to communicate, in an imperfect way, but better than having no language at all.