صورة الغلاف

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...

وصف كامل

محفوظ في:
التفاصيل البيبلوغرافية
المؤلفون الرئيسيون: Monzur, Murtoza, Mohamad, Radziah, Saadon, Nor Azizah
التنسيق: مقال
منشور في: Springer Science and Business Media Deutschland GmbH 2021
الموضوعات:
QA75 Electronic computers. Computer science
الوصول للمادة أونلاين:http://eprints.utm.my/id/eprint/100252/
http://dx.doi.org/10.1007/978-3-030-70713-2_30
الوسوم: إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
id my.utm.100252
record_format eprints
spelling my.utm.1002522023-03-29T07:04:46Z http://eprints.utm.my/id/eprint/100252/ 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 Article 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. Lecture Notes on Data Engineering and Communications Technologies, 72 (NA). pp. 314-323. ISSN 2367-4512 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 Article
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/100252/
http://dx.doi.org/10.1007/978-3-030-70713-2_30
_version_ 1762392180206338048
score 13.251813

مواد مشابهة