Semantic modelling plays an important role in data processing, enabling a deep understanding of information and the development of intelligent systems. One of the methods is a four-level model of knowledge representation including ontological, theoretical, empirical and statistical levels. The problem of incomplete knowledge makes it difficult to describe axioms in an object domain. The paper discusses an approach in which a precedent model (third level) is created based on precedent knowledge and then, through its fuzzification, statistical knowledge (fourth level) is obtained. This probabilistic knowledge is objective. However, in some domains subjective expert estimates may also be used. In such cases, the process starts with the creation of a blurry (fuzzy) model. The paper proposes a mathematical apparatus for reconstructing a set of precedents based on these estimates and describes the properties of blurry models.