Etude et analyse des méthodes d'apprentissage pour les données sémantiques à grande échelle
| dc.contributor.author | BOUGHAREB, Rima | |
| dc.date.accessioned | 2024-03-19T13:59:41Z | |
| dc.date.available | 2024-03-19T13:59:41Z | |
| dc.date.issued | 2023 | |
| dc.description.abstract | The expansion of the World Wide Web (WWW) has significantly changed how people produce and consume information. However, it is evolving beyond being a simple web of documents that only people can understand. With the advent of the Semantic Web, the web's capabilities have been further enhanced by moving from a web of interconnected web pages primarily intended for human consumption to a more sophisticated web of Linked Data that is both machine-readable and human-understandable. This evolution has unlocked new possibilities for knowledge integration, discovery, and automation particularly since Tim Berners-Lee, the mind behind the Web, introduced the Linked Open Data (LOD) Principles. The primary objective of the LOD initiative is to promote the open sharing and interlinking of diverse datasets. | |
| dc.identifier.uri | https://dspace.univ-annaba.dz//handle/123456789/3374 | |
| dc.language.iso | en | |
| dc.title | Etude et analyse des méthodes d'apprentissage pour les données sémantiques à grande échelle | |
| dc.type | Thesis |