A semantic matching approach addressing multidimensional representations for web service discovery

In recent years, discovering appropriate web services has become increasingly difficult as the number of services has grown rapidly. With the goal of improving discovery performance through accurate text matching, this study developed a service discovery method that constructs a neural matching network based on multidimensional service representations. Specifically, we performed data processing and adopted three methods called term frequency-inverse document frequency, Word2Vec, and ELMo to generate multidimensional representations for capturing the word frequency, static context features, and dynamic context features of each keyword. Based on these features, we calculated the