An ontology-based framework for multi-source network data integration, semantic alignment and artificial intelligent driven 3D visualization for enhanced analytical decision making

This article analyzes modern approaches to processing, integrating, and visualizing network data. A conceptual model has been developed for integrating, semantically matching, and displaying information in a heterogeneous structure stored in various databases, both in 2D and 3D, based on user queries. During the study, the processes of identifying, clustering, and ontological mapping of commonalities between data were algorithmically justified. As a result, the developed approach allows analyzing network data, automating decision-making processes, and improving the efficiency of visual analysis. This article highlights the results of research conducted at the intersection of artificial intelligence, data integration, and information systems.

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