Classification Based Parameter Association for Non-Redundant Annotation

Abstract

Semantic annotation of Web Services can facilitate the automated service discovery and composition. At present, however, many solutions suffer from redundant annotations or imprecise derived annotations. To solve the problem, we need to find the parameters that have equivalent semantics in a large number of Web Services. This paper proposes a classification based parameter association, laying the foundation for non-redundant annotation and amendment. Then the paper presents the non-redundant annotation in reduced parameter space. And the annotation results are expanded to the original parameter space. Finally a prototype tool is made to show all Web Services' information and parameter association information, by which we can manually amend the annotation results. Moreover, the amendment results will be applied to all parameters that are semantically equivalent. To evaluate the final annotation results, the paper gives a method based on service discovery. The experimental results show that the parameter association approach can accurately identify the equivalence between parameters. It also suggest that nonredundant annotation and amendment can greatly reduce the workload, and can augment the semantics of Web Services with adequate accuracy, which can achieve similar performance in service discovery as OWLS services do.