Hybrid deep learning and similarity measures for requirements-driven composition of semantic web services

The composition of web service is the best chance provided by Service-Oriented Computing and Service-Oriented Architecture as it gives real competitive benefits for some industrial and technological actors via presenting them with the probability to guarantee fast and inexpensive improvement of collaborative and distributed software applications. Here, a novel technique is introduced, which contains several phases for better web services. At first, the requirements specification phase is enabled with a set of requirements such as non-functional and functional requirements. Next to the requirements specification stage, the discovery stage is enabled to choose the suitable web services that have high-matching profiles with the developer’s requirement set. Here, for a semantic matching algorithm, a new hybrid similarity measure is developed. Additionally, among the group of candidate services that the discovery phase returned, the best service is selected during the selection step. Then, hybrid Squeeze_Long Short-Term Memory (Squeeze_LSTM) is used for choosing the best service and it is designed by the formation of SqueezeNet and LSTM. The Semantic Web Services are finally implemented. The efficiency of the Squeeze_LSTM is evaluated and has achieved a superior precision of 0.909, recall of 0.890, and response time of 6.461S.