Combat capability graphical description method by ontology extension UML

Gang Zhu; Jiancheng Zheng; Zhihuai Li; Chunhe Chang; Hua Liu

doi:10.1117/12.2692449

23 August 2023 Combat capability graphical description method by ontology extension UML

Gang Zhu, Jiancheng Zheng, Zhihuai Li, Chunhe Chang, Hua Liu

Proceedings Volume 12784, Second International Conference on Applied Statistics, Computational Mathematics, and Software Engineering (ASCMSE 2023); 127840T (2023) https://doi.org/10.1117/12.2692449
Event: 2023 2nd International Conference on Applied Statistics, Computational Mathematics and Software Engineering (ASCMSE 2023), 2023, Kaifeng, China

Abstract

For the issues of strong specialization, semantic duality, and unfavorable cross-domain communication of combat capabilities description, an ontology enhanced UML technique for graphical description of combat capabilities is presented. The application of ontology theory to create a conceptual model of combat capabilities is made more readable by the definition of a standard for UML-style graphical descriptions of ontologies. The specification is used to create a combat capability metamodel that eliminates semantic duality and unifies the top-level semantics of articulating combat capability ideas across all domains. In order to establish the combat capability description language, twelve viewpoints from the capability, combat and information perspectives were defined in conjunction with the needs of the combat capability domain description. The combat capability description language is built using the UML Profile technology. The example demonstrates how the model for defining combat capabilities takes into consideration the benefits of ontology formalization and precision as well as UML standards and communication ease.

Citation Download Citation

Gang Zhu, Jiancheng Zheng, Zhihuai Li, Chunhe Chang, and Hua Liu "Combat capability graphical description method by ontology extension UML", Proc. SPIE 12784, Second International Conference on Applied Statistics, Computational Mathematics, and Software Engineering (ASCMSE 2023), 127840T (23 August 2023); https://doi.org/10.1117/12.2692449

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