Analysis of Web Service Using Word Embedding by Deep Learning

Abstract

Service discovery is important issue when providing value-added services by composition. Existing approaches such as keyword or ontology matching have limitations within current Web services because these approaches are working based on isolated services. To solve this problem, calculating service relationship is needed. When we calculate it, 4 properties are usually considered, functional similarity, quality of service (QoS), association of invocation, and sociability. In our previous research, we could calculate functional similarity and QoS by ontology or global social service network [2]. But association of invocation and sociability has not been calculated from real world. In this research, we calculate them by using word embedding. Word embedding can find the relationship between services. In this research, we experiment to calculate similarity of Web API methods as services. By regarding the method call sequence as the input of word embedding, we observe how the method is related to other method. Finally, experimental results show that which method is related to other methods.