Co-attentive representation learning for web services classification

Abstract

The rapid adoption of services-related technologies, such as cloud computing, has lead to the explosive growth of web services. Automated service classification that groups web services by similar functionality is a widely used technique to facilitate the management and discovery of web services within a large-scale repository. The existing service classification approaches primarily focus on learning the isolated representations of service features but ignored their internal semantic correlations. To address the aforementioned issue, we propose a novel deep neural network with the Co-Attentive Representation Learning (CARL-Net) mechanism for effectively classifying services by learning interdependent characteristics of service without feature engineering. Specifically, we propose a service data augmentation mechanism by extracting informative words from the service description using information gain theory. Such a mechanism can learn a correlation matrix among embedded augmented data and description, thereby obtaining their interdependent semantic correlation representations for service classification. We evaluate the effectiveness of CARL-Net by comprehensive experiments based on a real-world dataset collected from ProgrammableWeb, which includes 10,943 web services. Compared with seven web service classification baselines based on CNN, LSTM, Recurrent-CNN, C-LSTM, BLSTM, ServeNet and ServeNet-BERT, the CARL-Net can achieve an improvement of 5.66%–172.21% in the F-measure of web service classification.