A semantic web service classification system

• المؤلف الرئيسي: Tan, Jui Kian.
• مؤلفون آخرون: Goh Eck Soong, Angela
• التنسيق: Final Year Project
• اللغة: English
• منشور في: 2010
• الموضوعات: DRNTU::Engineering::Computer science and engineering::Information systems::Information storage and retrieval
• الوصول للمادة أونلاين: http://hdl.handle.net/10356/36278
• المؤسسة: Nanyang Technological University

Semantic Web Services allows web services to be searchable through discovery, composition and invocation and monitoring. Existing systems, such as MODiCo [1], allows automatic semantic web service discovery and composition but at high computational costs. By considering only web services in the domain of interest, the effectiveness and efficiency of web service discovery and composition are expected to be improved significantly. Hence, a Semantic Web Service Classification System is proposed. The classification system is designed with high speed performance and classification accuracies in mind. With these two criteria in mind, pure textual descriptions approach has been selected as the main approach in dealing with the task of semantic web service classification. Supervised machine learning algorithms are used with this approach. Experiments have shown that implementing a semantic web service classification system for existing systems, such as MODiCo, is feasible. Our approach is able to achieve good classification accuracies and speed performance using SVM as the machine learning algorithm. Top three classification results can be used to further improve the classification system. Further work such as multi-labeled classification methods and optimization of machine learning algorithms are areas worth researching.