Web service embedding: Representing the invocation association between services with practical-valued vectors

Service representation using specified methods to express the service's functionalities and non-functionalities in a machine-understandable format is crucial in service composition. In representing non-functionalities, existing approaches focus on QoS (Quality of Service) or sociability. The invocation association between services has not been considered in service representation. However, as the invocation association reflects the actual invocation situation on service execution, the inconsideration will increase the failure of service composition. Thus, finding an appropriate method to represent the invocation association is a significant issue in service composition. This paper proposes a novel service representation method called web service embedding (WSE) to generate a practical-valued vector representation using deep learning sequence models to address the challenge. First, we propose utilizing deep learning sequence models to capture the invocation association from service composition/invocation sequences. Next, the representation vectors of services can be generated using the pre-trained models by designing an unsupervised training solution. Then, two presented metrics, context invocation relevance, and semantic invocation relevance, are used to measure how well the embedding vectors of the models express the invocation association for clustering in service composition. Finally, various neural sequence architectures have been inspected to find suitable models for WSE. The experimental results show that our proposed WSE can successfully perform service representation for clustering based on the invocation association.