Similarity-Based Dynamic Multi-Dimension Concept Mapping Algorithm

Ontologies as the powerful foundation for structuring and reasoning domain knowledge have been generally recognized. Owing to the nature of heterogeneity and dynamics, the interaction of different ontologies in different domains is becoming a key issue in multi-agent system, information integration, semantic web and knowledge management and so on. With this background, this paper presents a similarity-based multi-dimension dynamic concept mapping algorithm S-Match. The proposed algorithm can execute dynamically the concept mapping at linguistic, structure, instance and reasoning levels according to the different requirements of flexibility and accuracy in the applications. The experiment shows that, S-Match, outperforms H-MATCH in precision and if needs much less experts knowledge than the GLUE approach.