Knowledge Graph Large Language Model (KG-LLM) for Link Prediction

This paper presents the Knowledge Graph Large Language Model (KG-LLM), a framework designed to improve multi-hop link prediction in knowledge graphs. The approach transforms graph structures into natural language prompts and applies instruction tuning to enable language models to reason over complex entity relationships. Experiments on standard datasets show that this method achieves higher accuracy and generalization compared to traditional techniques.