ServeNet: A Deep Neural Network for Web Services Classification

TL;DR

This paper introduces ServeNet, a deep neural network that automatically extracts high-level features from web service names and descriptions, significantly improving classification accuracy over traditional machine learning methods.

Contribution

The paper presents a novel deep neural network model that eliminates manual feature engineering for web service classification, handling variable-length inputs and outperforming existing methods.

Findings

Achieves higher accuracy than 10 traditional machine learning methods.

Demonstrates robustness across 10,000 real-world web services.

Effectively classifies services into 50 categories.

Abstract

Automated service classification plays a crucial role in service discovery, selection, and composition. Machine learning has been widely used for service classification in recent years. However, the performance of conventional machine learning methods highly depends on the quality of manual feature engineering. In this paper, we present a novel deep neural network to automatically abstract low-level representation of both service name and service description to high-level merged features without feature engineering and the length limitation, and then predict service classification on 50 service categories. To demonstrate the effectiveness of our approach, we conduct a comprehensive experimental study by comparing 10 machine learning methods on 10,000 real-world web services. The result shows that the proposed deep neural network can achieve higher accuracy in classification and more robust than other machine learning methods.