Version 1
: Received: 30 October 2024 / Approved: 30 October 2024 / Online: 31 October 2024 (09:59:42 CET)
How to cite:
Salem, H.; Salloum, H.; Sabbagh, K.; Mazzara, M. Enhancing News Articles: Automatic SEO Linked Data Injection for Semantic Web Integration. Preprints2024, 2024102489. https://doi.org/10.20944/preprints202410.2489.v1
Salem, H.; Salloum, H.; Sabbagh, K.; Mazzara, M. Enhancing News Articles: Automatic SEO Linked Data Injection for Semantic Web Integration. Preprints 2024, 2024102489. https://doi.org/10.20944/preprints202410.2489.v1
Salem, H.; Salloum, H.; Sabbagh, K.; Mazzara, M. Enhancing News Articles: Automatic SEO Linked Data Injection for Semantic Web Integration. Preprints2024, 2024102489. https://doi.org/10.20944/preprints202410.2489.v1
APA Style
Salem, H., Salloum, H., Sabbagh, K., & Mazzara, M. (2024). Enhancing News Articles: Automatic SEO Linked Data Injection for Semantic Web Integration. Preprints. https://doi.org/10.20944/preprints202410.2489.v1
Chicago/Turabian Style
Salem, H., Kamil Sabbagh and Manuel Mazzara. 2024 "Enhancing News Articles: Automatic SEO Linked Data Injection for Semantic Web Integration" Preprints. https://doi.org/10.20944/preprints202410.2489.v1
Abstract
This paper presents a novel solution aimed at enhancing news web pages for seamless integration into the Semantic Web. By utilizing advanced pattern mining techniques alongside OpenAI’s GPT-3, we rewrite news articles to improve their readability and accessibility for Google News aggregators. Our approach is characterized by its methodological rigor and is evaluated through quantitative metrics, validated using Google’s Rich Results Test, which confirms the effectiveness of our generated structured data. The impact of our work is threefold: it advances the technological integration of a substantial segment of the web into the Semantic Web, promotes the adoption of Semantic Web technologies within the news sector, and significantly enhances the discoverability of news articles in aggregator platforms. Furthermore, our solution facilitates the broader dissemination of news content to diverse audiences. This submission introduces an innovative solution substantiated by empirical evidence of its impact and methodological soundness, thereby making a significant contribution to the field of Semantic Web research, particularly in the context of news and media articles.
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
