The Graph Language: How Knowledge Graphs Speak to Large Language Models

Abstract:Large Language Models (LLMs) excel at reasoning but benefit from grounding provided by Knowledge Graphs (KGs). However, integrating these paradigms is challenging. We introduce GRALAN, which enables KGs to speak directly in the LLM's semantic space through relational tokens that preserve graph structure. GRALAN-s trainable language mediator generates structured tokens for any frozen LLM, creating a foundation for knowledge-intensive applications. We demonstrate its effectiveness in question-answering by re-framing the task as entity classification over question-focused subgraphs. Experiments show that GRALAN significantly outperforms existing methods, particularly on complex multi-hop reasoning tasks, establishing a new paradigm for KG-LLM integration that maintains structural fidelity while leveraging LLMs' reasoning capabilities.

Artificial Intelligence (cs.AI)

arXiv:2608.01175 [cs.AI]

(or arXiv:2608.01175v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2608.01175 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

https://doi.org/10.1007/978-3-032-09527-5_3 Focus to learn more DOI(s) linking to related resources

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