Injecting Knowledge Graphs into Large Language Models

Understand

Integrating structured knowledge from Knowledge Graphs (KGs) into Large Language Models (LLMs) remains a key challenge for symbolic reasoning.

• Existing methods mainly rely on prompt engineering or fine-tuning, which lose structural fidelity or incur high computational costs.
• Building on recent encoding techniques which integrate graph embeddings within the LLM input as tokens, we extend this paradigm to the KG domain by leveraging Knowledge Graph Embedding (KGE) models, thus enabling graph-aware reasoning.
• Our approach is model-agnostic, resource-efficient, and compatible with any LLMs.