Semantic Service Clustering With Lightweight BERT-Based Service Embedding Using Invocation Sequences

Abstract

Service clustering is an efficient method for facilitating service discovery and composition. Traditional approaches based on the self-description documents for services usually utilize service signatures. In Web service composition, service clustering can also be performed by the invocation relationship between services. Therefore, based on the successful development of several embedding techniques for words in several contexts, a novel deep learning-based service embedding using invocation sequences is devised for service clustering. Moreover, many microservices are being created because of the rapid development of the Internet of Things (IoT), and edge, and fog computing. Following these developments, Web service composition based on these environments has emerged in abundance. More efficient lightweight approaches to analyze large numbers of services are necessary for service clustering. Consequently, a lightweight deep learning-based approach for the semantic clustering of service composition is presented to address these requirements. In this paper, we first propose the concept of service embedding to capture semantic information from invocation sequences. Second, we suggest using state-of-the-art neural language sequence models for service embedding and develop a corresponding lightweight Bidirectional Encoder Representations of Transformers (BERT)-based model. Next, combined with K-means clustering, the semantic clustering of service composition is evaluated. Finally, the experimental results show that the clustering process can be effectively performed by the lightweight BERT-based model.