This is (even though it doesn't look like it at first glance) a deeply philosophical question about the nature of 'meaning'. This answer is necessarily limited in scope.
There are many ways of representing knowledge, and countless formalisms have been developed since the early days of AI. Many of them are based on some kind of predicate calculus, ontologies, semantic networks (providing eg inheritance of features and part-of relationships), and they seem to work fine for limited domains.
One problem is the grounding: if you have a predicate isGreen(x), what does that actually mean? How is it related to isBlue(x)? Do you want to treat them similarly? If so, you need to represent this somehow. You quickly come to the point where you will need to encode all the world's knowledge in some generalised way. An impossible task.
Linguists have struggled with this for decades: what is the meaning of a particular sentence? Apart from the fact that every individual human will interpret a given sentence differently (based on their own life experience and culture), there are many aspects to 'meaning' that need representing: the 'factual' meaning, but also pragmatic, evaluative, and all sorts of other nuances. An innocent utterance, That's a nice Apple you've got there, could have a whole raft of meanings packed into it, all implicit. For example, the person probably likes apples, that one in particular, that apple looks like a tasty piece of fruit, the other person is the owner of the apple, and it might also be a request which is intended to prompt the other person to offer it to you. How are you going to represent all that meaning?
One area that interests me personally is representing narrative events. This can — up to a point — be done using Conceptual Dependency, which uses a limited set of semantic primitives. While useful to encode basic stories, you cannot easily use it to represent the fact that grass is green.
So the answer is: there is no answer. AI is too broad a field, and you need to look at a particular application to decide which knowledge is relevant to it, and then how it can best be represented. There is a reason why there are so many ways of representing knowledge.
PS: You suggest this would be more precise. My personal view is that precision here is a red herring. The word green is not precise, as it covers a range of wave lengths, and different people would disagree on whether something is green or not. So a predicate isGreen(x) is not any more precise than that. Hence the appeal of fuzzy logic, which allows computation to be based on less precision.