Knowledge graphs, the backbone of many AI systems, are notoriously complex and time-consuming to build. Imagine a world where building these intricate webs of information is dramatically faster and easier. Recent research explores how large language models (LLMs) could revolutionize knowledge graph and ontology engineering, potentially automating tasks that currently demand extensive human expertise. The key? A modular approach. Traditionally, building ontologies—the schemas that define knowledge graphs—involved painstakingly linking thousands of concepts. This research suggests that by breaking down ontologies into smaller, digestible modules, LLMs can be far more effective. Think of it like assembling a puzzle: it's much easier when you can focus on smaller sections at a time. This modularity has shown promising results in early experiments. For instance, in complex ontology alignment, a task crucial for integrating different knowledge graphs, a modular approach boosted accuracy from near failure to over 95%. Similar gains were observed in automatically populating knowledge graphs with information extracted from text. By providing the LLM with a focused context within a module, the system achieved a 90% extraction rate for relevant information. While these findings are exciting, significant challenges remain. How can we best design these modules? What are the limitations of LLMs in this context? And how can we ensure the accuracy and consistency of the generated knowledge graphs? This research opens a new frontier in knowledge graph engineering, promising to accelerate the development of more powerful and sophisticated AI systems. As LLMs continue to evolve, their potential to automate and streamline knowledge graph creation could unlock a new era of AI capabilities.