| Revista: | Computación y sistemas |
| Base de datos: | |
| Número de sistema: | 000607918 |
| ISSN: | 1405-5546 |
| Autores: | Meghazi, Hadj Madani1 Mostefaoui, Sid Ahmed1 Maaskri, Moustafa1 Aklouf, Youcef3 |
| Instituciones: | 1University of Tiaret, Tiaret. Argelia 2University of Tiaret, Laboratory of Research in Artificial Intelligence and Systems, Argelia 3University of Science and Technology Houari Boumediene, Algiers. Argelia |
| Año: | 2024 |
| Periodo: | Abr-Jun |
| Volumen: | 28 |
| Número: | 2 |
| Paginación: | 529-542 |
| País: | México |
| Idioma: | Inglés |
| Resumen en inglés | Due to the rising number of firms and organizations offering access to their business data or resources on the internet through APIs, there has been a significant increase in the number of web APIs. This poses a difficulty in swiftly and effectively finding online APIs. In order to tackle this problem, the introduction of service classification has been implemented to streamline the process of finding services within a vast array of options. Prior approaches have endeavored to classify web services based on semantic characteristics, although their precision has been constrained. This work introduces a novel strategy named “DeepLAB-WSC” to improve the identification of web services. The approach specifically emphasizes actions derived from textual descriptions of web services and utilizes advanced techniques from deep learning-based text classification. The suggested methodology was evaluated using a real-world web API dataset and achieved superior results compared to existing state-of-the-art research. |
| Keyword: | Service classification, Action extraction, Text classification, Deep learning, Web services discovery |
| Texto completo: | Texto completo (Ver PDF) Texto completo (Ver HTML) |
