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• W3215067654 abstract "Security ontology can be used to build a shared knowledge model for an application domain to overcome the data heterogeneity issue, but it suffers from its own heterogeneity issue. Finding identical entities in two ontologies, i.e., ontology alignment, is a solution. It is important to select an effective similarity measure (SM) to distinguish heterogeneous entities. However, due to the complex semantic relationships among concepts, no SM is ensured to be effective in all alignment tasks. The aggregation of SMs so that their advantages and disadvantages complement each other directly affects the quality of alignments. In this work, we formally define this problem, discuss its challenges, and present a problem-specific genetic algorithm (GA) to effectively address it. We experimentally test our approach on bibliographic tracks provided by OAEI and five pairs of security ontologies. The results show that GA can effectively address different heterogeneous ontology-alignment tasks and determine high-quality security ontology alignments." @default.
• W3215067654 created "2021-12-06" @default.
• W3215067654 creator A5046867232 @default.
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• W3215067654 date "2021-11-30" @default.
• W3215067654 modified "2023-10-10" @default.
• W3215067654 title "Matching Cyber Security Ontologies through Genetic Algorithm-Based Ontology Alignment Technique" @default.
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• W3215067654 doi "https://doi.org/10.1155/2021/4856265" @default.
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