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- W4385196623 abstract "Web services have significantly expanded and become a key enabling technology for online data, application and resource sharing. Designing new methods for efficient and reliable web service recommendation has been of tremendous importance with the growing usage and prominence of web services. It would be ideal for a system to suggest online services that are in line with consumers’ preferences without requesting specific query information from them. Quality of Service (QoS) is vital for characterising non-functional aspects of Web services as they become more prevalent and widely used on the World Wide Web. The KnowSOntoWSR framework, which is built on a knowledge-driven and semantically inclined model that adheres to QoS ontology, is proposed in this research. AWS and WebSphere are employed as knowledge tags, and the powerful machine learning classifier XGBoost is applied. The features and recommendations are computed using the Twitter semantic similarity. The proposed framework outperforms the baseline models’ estimates with an accuracy of 95.94% and average F-measure of 95.93%." @default.
- W4385196623 created "2023-07-25" @default.
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- W4385196623 date "2023-01-01" @default.
- W4385196623 modified "2023-09-25" @default.
- W4385196623 title "KnowSOntoWSR: Web Service Recommendation System Using Semantically Driven QoS Ontology-Based Knowledge-Centred Paradigm" @default.
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- W4385196623 doi "https://doi.org/10.1007/978-981-19-9719-8_19" @default.
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