The rapid growth in scientific publications makes it increasingly difficult for researchers to stay up-to-date with the latest findings. Much of the valuable information—such as methods, datasets, and results—remains embedded in unstructured text, which limits its accessibility. In this presentation, we explore how large language models (LLMs) and knowledge graphs can work together to extract key scientific insights and connect research across different fields. We will also discuss how AI-driven recommendation systems, using these technologies, can help researchers find more relevant and transparent recommendations, making the research discovery process more efficient.