A semantic web service classification system

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...

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Main Author: Tan, Jui Kian.
Other Authors: Goh Eck Soong, Angela
Format: Final Year Project
Language:English
Published: 2010
Subjects:
Online Access:http://hdl.handle.net/10356/36278
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-362782023-03-03T20:34:13Z A semantic web service classification system Tan, Jui Kian. Goh Eck Soong, Angela School of Computer Engineering DRNTU::Engineering::Computer science and engineering::Information systems::Information storage and retrieval 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. Bachelor of Engineering (Computer Engineering) 2010-04-30T02:35:03Z 2010-04-30T02:35:03Z 2010 2010 Final Year Project (FYP) http://hdl.handle.net/10356/36278 en Nanyang Technological University 63 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Computer science and engineering::Information systems::Information storage and retrieval
spellingShingle DRNTU::Engineering::Computer science and engineering::Information systems::Information storage and retrieval
Tan, Jui Kian.
A semantic web service classification system
description 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.
author2 Goh Eck Soong, Angela
author_facet Goh Eck Soong, Angela
Tan, Jui Kian.
format Final Year Project
author Tan, Jui Kian.
author_sort Tan, Jui Kian.
title A semantic web service classification system
title_short A semantic web service classification system
title_full A semantic web service classification system
title_fullStr A semantic web service classification system
title_full_unstemmed A semantic web service classification system
title_sort semantic web service classification system
publishDate 2010
url http://hdl.handle.net/10356/36278
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