Deep Learning-Based Text Classification to Improve Web Service Discovery

Authors

  • Hadj Madani Meghazi University Of Science And Technology Houari Boumediene
  • Sid Ahmed Mostefaoui University of Tiaret
  • Moustafa Maaskri University of Tiaret
  • Youcef Aklouf University of Science And Technology Houari Boumediene

DOI:

https://doi.org/10.13053/cys-28-2-4556

Keywords:

Service classification, Action extraction, Text classification, Deep learning, Web services discovery

Abstract

As more companies and organizations make their business data or resources available on the web through APIs, the number of web APIs has grown substantially. This makes it challenging to locate web APIs quickly and efficiently. To address this issue, service classification has been introduced to simplify the discovery process among a large pool of services. Previous methods have attempted to categorize web services using semantic features, but they have been limited in accuracy. To enhance the discovery of web services, this study proposes a new approach called "DeepLAB-WSC", which focuses on actions extracted from textual descriptions of web services and leverages techniques from deep learning-based text classification. The proposed approach was tested on a real-world web API dataset and produced results that surpass current state-of-the-art works.

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Published

2024-06-12

Issue

Vol. 28 No. 2 (2024): 28(2) 2024

Section

Articles

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