It has generally been assumed that the knowledge employed by an AI reasoning system needs to be in an unambiguous, formally interpretable form. From that perspective, general knowledge expressed in ordinary language (e.g., “dogs bark”) is unacceptably ambiguous and incomplete. However, we can achieve at least a partial transformation of such knowledge into formal, generically quantified sentences by taking account of properties of words and phrases such as the aspectual category, tense, Levin class, and presuppositions of verbs, or the classification of predicates (adjectival, nominal, verbal) as applicable to objects or kinds of objects.