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.
Highlights
Security ontology builds a shared knowledge model for an information system’s security area to facilitate the establishment of trust relationships [1]
Due to the complex semantic relationships among concepts, no similarity measure (SM) is effective in all contexts
Being inspired by its success in the complex optimization domains [6, 7], we build a mathematical model under a parallel aggregating framework to define the security ontology alignment problem, propose a problem-specific genetic algorithm (GA) to address it, and determine high-quality security ontology alignments
Summary
Security ontology builds a shared knowledge model for an information system’s security area to facilitate the establishment of trust relationships [1]. E most flexible way to aggregate SMs is the parallel framework, which assigns a weight for each SM to obtain the final alignment. During this procedure, each SM’s similarity matrix is calculated, whose rows and columns, respectively, represent two ontologies’ entities and whose elements are their similarity values. Being inspired by its success in the complex optimization domains [6, 7], we build a mathematical model under a parallel aggregating framework to define the security ontology alignment problem, propose a problem-specific GA to address it, and determine high-quality security ontology alignments. Fmeasure is the harmony mean of recall and precision On this basis, the security ontology alignment problem has the objective to maximize the f-measure, and the decision variable is X We choose the weighted average strategy to aggregate the SMs, which is the most popular and flexible method in the domain of information fusion of combining SMs. e other aggregating mechanisms, such as those in the field of evidential reasoning and fuzzy reasoning, could be applied, which is one of our future works
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