# Enhancing News Articles: Automatic SEO Linked Data Injection for Semantic Web Integration
## 7 Conclusions  
In this research, we have proposed a comprehensive solution for embedding linked data objects into news articles sourced from Google News. Our methodology employs pattern mining techniques to extract key content from news articles, including titles and bodies, which are then processed to create structured JSON-LD annotations. To enhance the accuracy and relevance of the extracted content, we used OpenAI's GPT-3 for cleaning, ensuring the removal of irrelevant elements such as advertisements, links, and extraneous words. This approach allows for the generation of high-quality linked data, which facilitates the seamless integration of news content into the Semantic Web.  
The dataset used in this study comprised 1100 web pages from 18 distinct news websites, written in both English and Arabic. The results demonstrated a substantial similarity rate exceeding 93% for news titles and over 90% for the article body within the generated linked data JSON objects. We validated the accuracy of our generated JSON-LD by comparing it to the original using both the Google Rich Results Checker and a word-by-word comparison, achieving strong content fidelity.  
Our approach has the potential to be integrated as a plugin within various content management systems (CMSs), automating the injection of structured data annotations and enhancing the discoverability and interoperability of news articles on the web. Additionally, the versatility of the technique suggests broader applicability for embedding linked data in a variety of web page types, benefiting both developers and end users by improving web content functionality and searchability.  
However, there are limitations in our current approach, including the reliance on predefined rules for pattern mining and text extraction based on HTML attributes. These rules may not perform optimally on websites with complex or dynamic layouts. While GPT-3 has proven effective in cleaning content, there are occasional instances of relevant information being erroneously filtered out. These challenges indicate the need for more adaptive algorithms capable of handling diverse and evolving web page structures.  
In future work, we aim to address these limitations by exploring machine learning-based adaptive extraction techniques, enhancing the model's ability to generalize across different web structures. Expanding our method to support multilingual content, multimedia (including images and videos), and fact-checking mechanisms will further improve the reliability and versatility of the solution. Additionally, we plan to incorporate real-time user feedback to refine the content cleaning process and ensure compliance with emerging standards for linked data on the web.  
Looking forward, we will continue to expand our dataset and refine the accuracy of our algorithm. We believe our research has significant potential to advance the adoption of linked data practices across the web, fostering improved information accessibility, integration, and semantic understanding.  
Conceptualization, H.S. ([PERSON]); Methodology, H.S. ([PERSON]), H.S. ([PERSON]) and K.S.; Software, H.S. ([PERSON]); Validation, H.S. ([PERSON]); Data curation, O.O.; Writing--original draft, H.S. ([PERSON]), H.S. ([PERSON]) and M.M.; Writing--review & editing, H.S. ([PERSON]), K.S. and M.M.; Supervision, H.S. ([PERSON]) and M.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflicts of interest.