Abstract
This paper presents a method of semantic modeling based on representing complex objects and phenomena as informational aggregates composed of core, designating, characterizing, and associative entities. Each entity is described by a set of attributes, and their interactions are governed by semantic operation algorithms that generate new informational structures. This approach enables a transition from formal syntactic data representation to meaningful semantic interpretation. The proposed method can be applied to the development of expert and cognitive systems that perform machine understanding and semantic information processing within the field of artificial intelligence.