Web services classification via combining Doc2Vec and LINE model

Classifying web services with similar functionality from tremendous amount of web services can significantly improve the efficiency of service discovery. Few of the web services classification researches integrate the independent mining of the content semantic information and network structure information hidden in the web service characterisation documents. Therefore, we propose a web service classification method combining them. So, the Doc2Vec algorithm is firstly exploited to deeply mine the functional semantics of web service characterisation documents and obtain web service's content semantic representation. Then, the LINE algorithm is adopted to embed the web service information network which is constructed by utilising web service characterisation documents and word information. Subsequently, the content semantic representation and network structure representation of web service are integrated as the input of the logistic regression classifier to perform web service classification. The experimental results on the ProgrammableWeb dataset verify that the proposed method outperforms to baseline methods.