In this article, Deconstructing Knowledge Graphs and Large Language Models, published in DATAVERSITY, Andreas Blumauer, SVP Growth and Marketing at Graphwise, explores how knowledge graphs and large language models (LLMs) can work together to create more powerful AI systems.
Knowledge graphs structure real-world information as networks of connected entities and relationships, providing semantic meaning that helps machines understand data rather than just process it. LLM excel at generating human-like text by learning from vast datasets, but they suffer from hallucinations, bias, and lack of factual grounding since they rely primarily on statistical patterns. When combined, knowledge graphs provide the structured foundation that LLMs need for accuracy and consistency, while LLMs offer generative capabilities that can help expand and refine knowledge graphs.
Looking ahead, Andreas envisions a future where these technologies create recursively self-improving AI systems, with LLMs using knowledge graphs as grounding mechanisms to avoid drift and false information. Still, this integration requires human oversight to ensure ethical development and alignment with societal values. Together, these complementary technologies can bridge the gap between pattern recognition and genuine understanding, creating AI systems that balance creativity with reliability and adapt over time while maintaining factual accuracy.