Genetic Algorithm With Hill Climbing for Correspondences Discovery in Ontology Mapping

Meta-heuristics are used as a tool for ontology mapping process in order to improve their performance in mapping quality and computational time. In this article, ontology mapping is resolved as an optimization problem. It aims at optimizing correspondences discovery between similar concepts of source and target ontologies. For better guiding and accelerating the concepts correspondences discovery, the article proposes a meta-heuristic hybridization which incorporates the Hill Climbing method within the mutation operator in the genetic algorithm. For test concerns, syntactic and lexical similarities are used to validate correspondences in candidate mappings. The obtained results show the effectiveness of the proposition for improving mapping performances in quality and computational time even for large OAEI ontologies.

• ontology mapping
• genetic algorithm
• hill climbing
• metaheuristic optimization
• hybrid metaheuristic
• syntactic similarity
• word similarity
• mapping quality
• computational efficiency

• Semantic Web and Ontologies
• Biomedical Text Mining and Ontologies
• Natural Language Processing Techniques
• ontology mapping
• genetic algorithm
• hill climbing
• metaheuristic optimization
• hybrid metaheuristic
• syntactic similarity
• word similarity
• mapping quality
• Artificial Intelligence
• Molecular Biology
• Computer Science
• Biochemistry, Genetics and Molecular Biology