However, since distributional models represent words as aggregated distributions of their contexts, and compute semantic similarity from these context distributions, the contexts that they use need to be generic enough to yield meaningful overlap between concepts (e.g., words).
distributional models cannot easily represent knowledge of the form "the value for property Y of word/concept X is Z".