# Enhancing News Articles: Automatic SEO Linked Data Injection for Semantic Web Integration
## 1 Introduction  
The Semantic Web is a visionary extension of the traditional World Wide Web, aimed at making data more accessible and interpretable by machines. As a transformative approach, the Semantic Web embeds structured, machine-readable information into web content, enabling both artificial and human agents to understand and navigate content effectively [1, 2]. This capability is particularly impactful in fields like e-learning, as well as news web portals, where structured data improve article indexing and presentation, facilitating platforms like Google News in enhancing content discoverability and relevance through structured data integration [3, 4]. One key advancement in this area is rich results, which enable search engines to present enhanced, visually informative search results that may include images, star ratings, and other contextual information. These rich results rely on structured data annotations embedded directly within the HTML of web pages, typically using formats like JavaScriptObject Notation for Linked Data (JSON-LD). JSON-LD is a lightweight linked data format that allows website developers to add structured metadata to web content in a way that is both machine-readable and straightforward to implement. By embedding JSON-LD annotations, web developers can provide essential details to search engines without altering visible content, ensuring better indexing and enhancing search visibility [5; 6]. Such structured data significantly improve a website's search engine optimization (SEO), a collection of techniques aimed at enhancing a website's visibility and ranking on search engine results pages (SERPs). SERPs are the pages displayed by search engines in response to a user's query, and they are instrumental in attracting online traffic by presenting a ranked list of relevant links, summaries, and often enhanced features like images or videos. By optimizing elements like keywords, content, and links, SEO strategies help elevate a website's SERP position, which is critical for online visibility and user engagement [7]. JSON-LD's compatibility with SEO efforts allows it to boost site visibility by making important data accessible for indexing [8]. However, many legacy websites lack these structured data annotations, making it challenging for search engines to accurately index their content, thereby negatively impacting SEO performance, visibility, and user engagement [9; 10; 11]. Legacy news websites often lack the tools and resources to implement structured data, resulting in poor visibility and limited engagement on search platforms. This creates a significant barrier to information accessibility and discoverability, as valuable content becomes harder to access for users relying on modern search engines. By addressing this gap, our research seeks to transform how legacy news content is indexed and presented, making essential information more accessible to the public. Motivated by this challenge, our research seeks to address the gap in structured data for legacy news websites, which are often excluded from modern CMS tools that facilitate structured data generation. This paper introduces a comprehensive solution to automatically generate JSON-LD scripts tailored for news websites, bridging the gap between unstructured and structured web data. Our approach is driven by two primary innovations. The first innovation is our use of advanced pattern mining techniques coupled with cutting-edge NLP models, specifically OpenAI's GPT-3. GPT-3 not only helps in accurately extracting relevant article features such as titles, dates, and content structure, but also enables content refinement by removing irrelevant text and advertisements. This ensures the annotations generated are clean, focused, and high-quality, making the structured data both accurate and SEO-optimized. Second, we introduce a robust validation framework to ensure that the generated structured data align with search engine standards.