Textbook2KG: Decoding Knowledge Graph from Very Long Textual Content via Prompt Engineering

We introduce Textbook2KG for building knowledge graphs from lengthy textbooks using large language models (LLMs) and smart prompts. Our framework uses a step-by-step approach: splitting texts, extracting key facts, and connecting concepts through reasoning. Tested on three real textbooks, it achieved $85.5 \%$ accuracy in direct fact extraction but $65 \%$ for inferred links, showing where LLMs shine and where they need help. The textbook datasets we created with $\mathbf{5 2 8 k} \boldsymbol{+}$ words analyzed give researchers a solid base to improve educational KG tools. While current models make KG creation easier for teachers, smarter reasoning remains a challenge. This work shows how simple prompt tweaks can unlock LLMs’ hidden skills for organizing academic knowledge, blending AI power with classroom needs.