  Large Language Models (LLMs) have demonstrated remarkable capabilities in
many real-world applications. Nonetheless, LLMs are often criticized for their
tendency to produce hallucinations, wherein the models fabricate incorrect
statements on tasks beyond their knowledge and perception. To alleviate this
issue, researchers have explored leveraging the factual knowledge in knowledge
graphs (KGs) to ground the LLM's responses in established facts and principles.
However, most state-of-the-art LLMs are closed-source, making it challenging to
develop a prompting framework that can efficiently and effectively integrate
KGs into LLMs with hard prompts only. Generally, existing KG-enhanced LLMs
usually suffer from three critical issues, including huge search space, high
API costs, and laborious prompt engineering, that impede their widespread
application in practice. To this end, we introduce a novel Knowledge Graph
based PrompTing framework, namely KnowGPT, to enhance LLMs with domain
knowledge. KnowGPT contains a knowledge extraction module to extract the most
informative knowledge from KGs, and a context-aware prompt construction module
to automatically convert extracted knowledge into effective prompts.
Experiments on three benchmarks demonstrate that KnowGPT significantly
outperforms all competitors. Notably, KnowGPT achieves a 92.6% accuracy on
OpenbookQA leaderboard, comparable to human-level performance.
