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Effective web service classification using a hybrid of ontology generation and machine learning algorithm

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

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Official URL: http://dx.doi.org/10.1007/978-3-030-70713-2_30

Abstract

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.

Item Type:Article
Uncontrolled Keywords:Machine learning, Naive Bayes (NB), Ontology, Service classification, Support Vector Machines (SVMs)
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Computing
ID Code:100252
Deposited By: Widya Wahid
Deposited On:29 Mar 2023 07:04
Last Modified:29 Mar 2023 07:04

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