In today's digital age, separating fact from fiction has become a critical challenge, especially with the rise of misinformation. While Large Language Models (LLMs) have shown promise in various applications, including fact-checking, they are not without limitations. These models can sometimes generate incorrect information ("hallucinations") and struggle to process information beyond their training data cut-off. Retrieval Augmented Generation (RAG) systems help address some of these challenges, but they too can be tripped up by noisy or contradictory data. Our research introduces a novel approach to fact-checking called CommunityKG-RAG, which leverages the power of knowledge graphs to enhance the accuracy and relevance of information retrieval. Imagine a vast network of interconnected facts and entities—that's essentially what a knowledge graph is. Our method uses this interconnectedness to its advantage. Traditional RAG systems often struggle to identify the most relevant information, especially when a crucial fact is buried deep within a long text. They also have difficulty navigating through information that's noisy or full of conflicting points of view. CommunityKG-RAG addresses these shortcomings by identifying "communities" of related information within the knowledge graph. Instead of sifting through an entire document, CommunityKG-RAG narrows its focus to these tight-knit clusters of relevant data. This targeted approach is particularly effective for fact-checking as it allows the system to quickly hone in on the information needed to verify a claim, even when that information is complex or multi-layered. Our experiments show that CommunityKG-RAG significantly outperforms traditional methods, opening up exciting new possibilities for the future of fact-checking. One of the key advantages of our approach is its scalability. It doesn't require extensive training or fine-tuning for every new dataset, which is a major plus. This makes it a practical solution for real-world applications where data is constantly changing. While our findings are promising, there are still areas for future improvement. Like many AI systems, CommunityKG-RAG requires substantial computing resources. Further research will focus on making the system more efficient and reducing its reliance on computationally intensive tasks. We’re excited about the potential of CommunityKG-RAG to contribute to a more informed world by making fact-checking faster, more accurate, and more accessible to everyone.