Web service selection based on ranking of qos using associative classification
Molood Makhlughian, Seyyed Mohsen Hashemi, Yousef Rastegari, Emad, Pejman

TL;DR
This paper proposes a framework for selecting and ranking web services by combining QoS classification with semantic matching, improving service selection accuracy based on user preferences.
Contribution
It introduces a novel service selection framework that integrates associative classification for QoS level classification with semantic similarity for ranking.
Findings
Framework effectively classifies services into QoS levels.
Semantic matching improves ranking accuracy.
Experimental results confirm satisfaction of user requirements.
Abstract
With the explosive growth of the number of services published over the Internet, it is difficult to select satisfactory web services among the candidate web services which provide similar functionalities. Quality of Service (QoS) is considered as the most important non-functional criterion for service selection. But this criterion is no longer considered as the only criterion to rank web services, satisfying user's preferences. The similarity measure (outputs-inputs similarity) between concepts based on ontology in an interconnected network of semantic Web services involved in a composition can be used as a distinguishing criterion to estimate the semantic quality of selected services for the composite service. Coupling the semantic similarity as the functional aspect and quality of services allows us to further constrain and select services for the valid composite services. In this…
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