LDNM: A General Web Service Classification Framework via Deep Fusion of Structured and Unstructured Features

Abstract

Classifying Web services plays a critical role in several fundamental service management tasks, such as service discovery, selection, ranking, and recommendation. However, traditional Web service classification approaches usually difficult to dispose unstructured sparse documents and underutilize the rich network relations. The consideration of multiple document representation schemes can ameliorate the former problem, whereas an appropriate network representation method could be a positive solution to the latter problem. In this paper, we propose a general Web service classification framework via deep fusion of structured and unstructured features, named LDNM. Firstly, we transform each service document into feature vectors by using two document representation methods: topic distribution based on LDA, and neural-network-based document embedding model known as Doc2vec. Then we obtain structured representation vectors which stem from service invoking and tagging graphs by applying Node2vec. Finally, we fuse these features and train a service classifier by using an MLP neural network. Comprehensive experiments are conducted on real-world datasets to demonstrate the effectiveness of the proposed approach.