How to Build a Knowledge Graph in the Age of LLMs
Last Updated on August 28, 2025 by Editorial Team
Author(s): Michael Shapiro MD MSc
Originally published on Towards AI.
How to Build a Knowledge Graph in the Age of LLMs
In recent years, LLMs have transformed the way we do almost everything. Knowledge graphs(KGs) have been there since the digital revolution as a way store complex and interconnected information. However, as the amount and complexity of the data grew, the KG value in organizing and representing the connecting web of ideas became apparent. And as Steve Jobs said: “Creativity is just connecting things.”
The article discusses the growing importance of knowledge graphs (KGs) in the context of large language models (LLMs). It emphasizes that while KGs have been instrumental in organizing complex information, the advancements in LLMs present both opportunities and challenges. The article outlines various tasks involved in building KGs—ranging from unstructured information extraction to ontology alignment, data validation, and filling missing information. LLMs significantly accelerate these tasks, allowing for a high level of semantic understanding that improves performance and scalability, though they also introduce potential errors that necessitate robust quality assurance measures.
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