An outranking model for web service discovery

Fethallah, Hadjila; Ismail, Smahi Mohamed; Mohamed, Merzoug; Zeyneb, Torchane

doi:10.1109/mathit.2017.8259711

Cited by 2 publications

(3 citation statements)

References 12 publications

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“…On the other hand, to validate our results against other approaches in the literature. We implemented two methods previously mentioned in the related works section [29], [30]. For the approach proposed in [29], we utilized the best interval judged by the authors.…”

Section: Results' Discussionmentioning

confidence: 99%

“…We implemented two methods previously mentioned in the related works section [29], [30]. For the approach proposed in [29], we utilized the best interval judged by the authors. Figure 4 shows a comparison of precision, recall, and F-measure for the mentioned methods.…”

Section: Results' Discussionmentioning

confidence: 99%

“…The work done in [29] allocated the web service discovery problem based on integration algorithms. The contribution consists of computing the best WSs following an outranking relationship.…”

Section: Related Work and Motivationmentioning

confidence: 99%

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A semantic web services discovery approach integrating multiple similarity measures and k-means clustering

Fariss¹,

Allali²,

Asaidi³

et al. 2021

IJEECS

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Web service (WS) discovery is an essential task for implementing complex applications in a service oriented architecture (SOA), such as selecting, composing, and providing services. This task is limited semantically in the incorporation of the customer’s request and the web services. Furthermore, applying suitable similarity methods for the increasing number of WSs is more relevant for efficient web service discovery. To overcome these limitations, we propose a new approach for web service discovery integrating multiple similarity measures and k-means clustering. The approach enables more accurate services appropriate to the customer's request by calculating different similarity scores between the customer's request and the web services. The global semantic similarity is determined by applying k-means clustering using the obtained similarity scores. The experimental results demonstrated that the proposed semantic web service discovery approach outperforms the state-of-the approaches in terms of precision (98%), recall (95%), and F-measure (96%). The proposed approach is efficiently designed to support and facilitate the selection and composition of web services phases in complex applications.

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Section: Results' Discussionmentioning

confidence: 99%