An Approach to Granulation and Ontology Similarity Measurement for Intelligent Decision Support in Semi-Structured Subject Areas

The semi-structured subject areas are one of the main study objects in the theory of artificial intelligent systems. As the largest intelligent management system in a semi-structured area, an intelligent railway transport management system is considered. The task of the constructing granular ontologies for a semi-structured domain that is different from the known device and data synchronization between granules is examined. An approach to measuring the similarity degree is proposed by the performed measuring analysis of the semantic proximity of the ontology contexts. The proposed approach is based on the graph representation of some ontologies; it is universal and applicable for intelligent decision support in semi-structured subject areas.

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