Title: REL-RAG: Relation-Aware Retrieval-Augmented Generation for Generalizable Knowledge Graph Question Answering
Primary area: learning on graphs and other geometries & topologies
Keywords: KGQA, RAG, graph neural networks, graph retriever, line graph

Abstract: Large Language Models (LLMs) augmented with knowledge graphs (KGs) have been widely studied for knowledge graph question answering (KGQA). Graph-based retrievers exhibit strong empirical performance, but their generalization ability remains limited.  
In this work, we show that applying a *line graph transformation* to the KG provably enhances the generalizability of GNN-based retrievers. By elevating relations to first-class objects, line graphs encode relation transitions explicitly, and the resulting inductive bias aligns naturally with relational reasoning in KGs. This alignment makes multi-hop reasoning substantially easier to learn and improves generalizability across different types of distribution shifts.  Building upon this representation, we propose $\texttt{REL-RAG}$, a framework that emphasizes relational reasoning for graph retrievers and is equipped with two complementary training objectives for flexible integration with LLMs. Path-based learning achieves higher precision with fewer tokens, making it especially suitable for smaller LLMs with limited context capacity. Triple-based learning encourages richer evidence diversity, which stronger LLMs can exploit more effectively with larger token budgets.  Empirically, $\texttt{REL-RAG}$ establishes new state-of-the-art results on KGQA benchmarks, surpassing prior graph retrievers by up to $20.3\\%$ with Llama3.1-8B and $10.3\\%$ with GPT-4o-mini.