Effective web service classification using a hybrid of ontology generation and machine learning algorithm
Efficient and fast service discovery becomes an extremely challenging task due to the proliferation and availability of functionally-similar web services. Service classification or service grouping is a popular and widely applied technique to classify services into several groups according to simila...
Saved in:
Main Authors: | , , |
---|---|
Format: | Book Section |
Published: |
Springer Science and Business Media Deutschland GmbH
2021
|
Subjects: | |
Online Access: | http://eprints.utm.my/id/eprint/96930/ http://dx.doi.org/10.1007/978-3-030-70713-2_30 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
my.utm.96930 |
---|---|
record_format |
eprints |
spelling |
my.utm.969302022-09-04T06:56:11Z http://eprints.utm.my/id/eprint/96930/ Effective web service classification using a hybrid of ontology generation and machine learning algorithm Monzur, Murtoza Mohamad, Radziah Saadon, Nor Azizah QA75 Electronic computers. Computer science Efficient and fast service discovery becomes an extremely challenging task due to the proliferation and availability of functionally-similar web services. Service classification or service grouping is a popular and widely applied technique to classify services into several groups according to similarity, in order to ease up and expedite the discovery process. Existing research on web service classification uses several techniques, approaches and frameworks for web service classification. This study focused on a hybrid service classification approach based on a combination of ontology generation and machine learning algorithm, in order to gain more speed and accuracy during the classification process. Ontology generation is applied to capture the similarity between complicated words. Then, two machine learning classification algorithms, namely, Support Vector Machines (SVMs) and Naive Bayes (NB), were applied for classifying services according to their functionality. The experimental results showed significant improvement in terms of accuracy, precision and recall. The hybrid approach of ontology generation and NB algorithm achieved an accuracy of 94.50%, a precision of 93.00% and a recall of 95.00%. Therefore, a hybrid approach of ontology generation and NB has the potential to pave the way for efficient and accurate service classification and discovery. Springer Science and Business Media Deutschland GmbH 2021 Book Section PeerReviewed Monzur, Murtoza and Mohamad, Radziah and Saadon, Nor Azizah (2021) Effective web service classification using a hybrid of ontology generation and machine learning algorithm. In: Innovative Systems for Intelligent Health Informatics : Data Science, Health Informatics, Intelligent Systems, Smart Computing. Lecture Notes on Data Engineering and Communications Technologies, 72 (NA). Springer Science and Business Media Deutschland GmbH, NA, pp. 314-323. ISBN 978-3-030-70712-5 http://dx.doi.org/10.1007/978-3-030-70713-2_30 DOI : 10.1007/978-3-030-70713-2_30 |
institution |
Universiti Teknologi Malaysia |
building |
UTM Library |
collection |
Institutional Repository |
continent |
Asia |
country |
Malaysia |
content_provider |
Universiti Teknologi Malaysia |
content_source |
UTM Institutional Repository |
url_provider |
http://eprints.utm.my/ |
topic |
QA75 Electronic computers. Computer science |
spellingShingle |
QA75 Electronic computers. Computer science Monzur, Murtoza Mohamad, Radziah Saadon, Nor Azizah Effective web service classification using a hybrid of ontology generation and machine learning algorithm |
description |
Efficient and fast service discovery becomes an extremely challenging task due to the proliferation and availability of functionally-similar web services. Service classification or service grouping is a popular and widely applied technique to classify services into several groups according to similarity, in order to ease up and expedite the discovery process. Existing research on web service classification uses several techniques, approaches and frameworks for web service classification. This study focused on a hybrid service classification approach based on a combination of ontology generation and machine learning algorithm, in order to gain more speed and accuracy during the classification process. Ontology generation is applied to capture the similarity between complicated words. Then, two machine learning classification algorithms, namely, Support Vector Machines (SVMs) and Naive Bayes (NB), were applied for classifying services according to their functionality. The experimental results showed significant improvement in terms of accuracy, precision and recall. The hybrid approach of ontology generation and NB algorithm achieved an accuracy of 94.50%, a precision of 93.00% and a recall of 95.00%. Therefore, a hybrid approach of ontology generation and NB has the potential to pave the way for efficient and accurate service classification and discovery. |
format |
Book Section |
author |
Monzur, Murtoza Mohamad, Radziah Saadon, Nor Azizah |
author_facet |
Monzur, Murtoza Mohamad, Radziah Saadon, Nor Azizah |
author_sort |
Monzur, Murtoza |
title |
Effective web service classification using a hybrid of ontology generation and machine learning algorithm |
title_short |
Effective web service classification using a hybrid of ontology generation and machine learning algorithm |
title_full |
Effective web service classification using a hybrid of ontology generation and machine learning algorithm |
title_fullStr |
Effective web service classification using a hybrid of ontology generation and machine learning algorithm |
title_full_unstemmed |
Effective web service classification using a hybrid of ontology generation and machine learning algorithm |
title_sort |
effective web service classification using a hybrid of ontology generation and machine learning algorithm |
publisher |
Springer Science and Business Media Deutschland GmbH |
publishDate |
2021 |
url |
http://eprints.utm.my/id/eprint/96930/ http://dx.doi.org/10.1007/978-3-030-70713-2_30 |
_version_ |
1743107047573946368 |
score |
13.160551 |