Summary: The article overviews the means for describing and formally analyzing natural-language text knowledge under uncertainty. We consider a family of classic attribute languages and logics based on them, their properties, problems, and solution tools. We also overview propositional \(n\)-valued logics and fuzzy logics, their syntax and semantics. Based on the considered logical constructions, we propose syntax and set-theoretic interpretation of \(n\)-valued description logic \(\mathit{ALCQ}_n\) that provides the means for describing concept intersection, union, complement, value restrictions, and qualitative and quantitative constraints. We consider the means for solving key problems of reasoning over such logics: executability, augmentation, equivalence, and disjunctivity. As an algorithm for calculating the executability degree, we consider an extension of the tableau algorithm often used for first-order logic with solving simple numerical constraints. We prove that the algorithm is terminal, complete, and non-contradictory. We also provide several applications for the formal representation in natural language processing, including extending results of machine learning models, combining knowledge from multiple sources and formally describing uncertain facts.