Revolutionizing website internal linking by leveraging cutting-edge data processing techniques, vector embeddings, and graph-based link prediction algorithms. By combining these advanced methodologies, the project aims to create an intelligent solution that optimizes internal link structures, improving SEO performance and user navigation.

We are incredibly grateful to our sponsors for their continued support in making this project possible. Their contributions have been vital in pushing the boundaries of what can be achieved through data-driven internal linking solutions.

• Kalicube.com: Special thanks to Kalicube.com for providing invaluable data, resources, and continuous encouragement. Your support has greatly enhanced the scope and impact of WebKnoGraph.
• WordLift.io: We extend our deepest gratitude to WordLift.io for their generous sponsorship and for sharing insights and data that were essential for this project’s success.
• Faculty of Computer Science and Engineering (FCSE) Skopje: A heartfelt thanks to FCSE Skopje professors, PhD Georgina Mircheva and PhD Miroslav Mirchev for their innovative ideas and technical suggestions. Their expertise and advisory during this were a key component in shaping the direction of WebKnoGraph.

Without the contributions from these amazing sponsors, WebKnoGraph would not have been possible. Thank you for believing in the vision and supporting the evolution of this groundbreaking project.

We welcome more sponsors and partners who are passionate about driving innovation in SEO and website optimization. If you're interested in collaborating or sponsoring, feel free to reach out!

WebKnoGraph is designed for tech-savvy marketers and marketing engineers with a strong understanding of advanced data analytics and data-driven marketing strategies. Our ideal users are professionals who have experience with Python or have access to development support within their teams.

These individuals are skilled in interpreting and utilizing data, as well as working with technical tools to optimize internal linking structures, improve SEO performance, and enhance overall website navigation. Whether directly coding or collaborating with developers, they are adept at leveraging data and technology to streamline marketing operations, increase customer engagement, and drive business growth.

If you're a data-driven marketer comfortable with using cutting-edge tools to push the boundaries of SEO, WebKnoGraph is built for you.

To explore and utilize WebKnoGraph, follow the instructions below to get started with the code, data, and documentation provided in the repository:

• Code: The core code for this project is located in the notebooks folder. These Jupyter notebooks contain the implementation of key components such as vector embeddings, graph-based link prediction algorithms, and data processing techniques.

• Data: The data used for analysis and testing is stored in the data folder. This includes all the datasets necessary to replicate the experiments and results outlined in the project.

• Technical Report: For a comprehensive understanding of the project, including the methodology, algorithms, and results, refer to the detailed technical report provided in the Technical_Report_Emilija_Gjorgjevska.pdf file. This document gives an in-depth coverage of the concepts and the execution of the solution.

By following these resources, you will gain full access to the materials and insights needed to experiment with and extend WebKnoGraph.