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Knowledge graphs have emerged as powerful tools for representing and organizing information in a structured and interconnected manner. In this research, we aim to build a knowledge graph and a recommendation system for research papers. This research is divided into three parts: Charting the Research Progress (constructing the RDF knowledge graph and visualizing), The Search for Knowledge (making a research paper recommender system), and revealing the Trailblazers (finding the most impactful paper in the dataset). By leveraging state-of-the-art tools and models, the knowledge graph was not only accurate but also visually intuitive, offering insights into the complex web of academic interrelations. The recommender system, built on this knowledge base, demonstrated high efficacy in suggesting relevant and significant research papers, with its recommendations being both contextually and academically pertinent. The impact analysis reveals the most influential papers, providing a clear view of research works that have shaped and directed academic discourse in their respective fields.
Vaidhyaraman et al. (Tue,) studied this question.