Informational ontological modeling

Objectives. Despite the wide application of the term “ontology” in philosophy and social sciences, ontological modeling in the fields of computer science and information theory remains poorly studied. The purpose of the work is to develop a methodology for the ontological modeling of information and to clarify the theory of information retrieval technology both in a broad sense and as part of ontological modeling. Relevant problems in ontological modeling include the necessity of demonstrating the difference between regularity and functional dependence.

Methods. To achieve the stated goal, a logically structural approach is used, including the construction of conceptual schemes and their description in terms of logical formalism. The logically structural approach includes the construction of conceptual schemes that serve to apply logical formalism. The basis of logical modeling involves the selection of related models. The extended information retrieval technology proposed for this purpose searches not for individual objects, but for groups of objects. Since ontological research is based on a transition from qualitative to quantitative description, the methods used include quantitative-qualitative transitions.

Results. A new concept of ontological modeling of information is introduced. The conditions of ontological modeling are substantiated. Relationships between the concepts of regularity and functionality are investigated. On this basis, an interpretation of regularity and functional dependence is given. Structural and formal differences between information modeling, information retrieval technologies, and ontological modeling are demonstrated. Three information retrieval tasks are described, of which the second and third tasks involving the search for a group of related objects and the search for relationships or connections within a group of related objects, respectively, are solved using ontological modeling. Formal schemes of ontological modeling are provided. The transition from relations to connections in the case of ontological modeling is demonstrated.

Conclusions. Ontological modeling is shown to be applicable only to related models or to models between which there is a commonality. A technology of ontological modeling is proposed, in which version information retrieval is the initial part, while the second option involves the use of cluster analysis technology. Since ontological modeling uses qualitatively quantitative transitions, the proposed variant can be used to extract implicit knowledge.

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