Service Classification Using Adaptive Back-Propagation Neural Network and Semantic Similarity

With the growing population of Web services, the discovery of services is a key to the development of Web services. While extensive researches focus mainly on service matchmaking algorithms, service classification that is also a meaningful approach to accelerating service discovery only receives little attention. In this paper, we propose to use adaptive back-propagation neural network model (BPM) to perform service recognition. During the training process, the feature vectors of training services and their categories are learned by the BPM. The element in the feature vector is the semantic similarity between the feature word in the system dictionary and the occurrence in the feature set for a service. During the recognition process, the characteristics of the test service are analyzed by the BPM and the output shows the category of the test service. Furthermore, the BPM is adapted with correctly recognized test services, which results in better modeling over time. Based on extensive experiments, we show that using the adaptive BPM is a promising way to realize automatic service classification, and the adaptive BPM using semantic similarity as the element in feature vector provides better performance than the general BPM using word frequency