S-Path-RAG: Semantic-Aware Shortest-Path Retrieval Augmented Generation for Multi-Hop Knowledge Graph Question Answering

Abstract

We present S-Path-RAG, a semantic-aware shortest-path Retrieval-Augmented Generation framework designed to improve multi-hop question answering over large knowledge graphs. S-Path-RAG departs from one-shot, text-heavy retrieval by enumerating bounded-length, semantically weighted candidate paths using a hybrid weighted $k$-shortest, beam, and constrained random-walk strategy, learning a differentiable path scorer together with a contrastive path encoder and lightweight verifier, and injecting a compact soft mixture of selected path latents into a language model via cross-attention. The system runs inside an iterative Neural-Socratic Graph Dialogue loop in which concise diagnostic messages produced by the language model are mapped to targeted graph edits or seed expansions, enabling adaptive retrieval when the model expresses uncertainty. This combination yields a retrieval mechanism that is both token-efficient and topology-aware while preserving interpretable path-level traces for diagnostics and intervention. We validate S-Path-RAG on standard multi-hop KGQA benchmarks and through ablations and diagnostic analyses. The results demonstrate consistent improvements in answer accuracy, evidence coverage, and end-to-end efficiency compared to strong graph- and LLM-based baselines. We further analyze trade-offs between semantic weighting, verifier filtering, and iterative updates, and report practical recommendations for deployment under constrained compute and token budgets.

Figures (8)

• Figure 1: Overview of S-Path-RAG. Given a natural language question, the system performs entity linking to obtain top-$m$ seed entities and enters an iterative Neural-Socratic Graph Dialogue loop. In each round, a GNN encodes the current subgraph, a weighted path enumerator proposes candidate paths, and a scorer with a verifier ranks their relevance. A soft mixture of selected path embeddings is injected into a frozen language model via cross-attention, and the resulting answer together with a diagnostic signal is mapped to graph edits that update the subgraph. The loop terminates when the model reaches sufficient confidence or the maximum number of rounds is reached.
• Figure 2: Answer coverage versus number of retrieved paths for different retrieval strategies.
• Figure 3: Attention heatmap showing token-to-path alignment (darker = higher attention).
• Figure 4: Answer coverage versus number of retrieved paths for different retrieval strategies.
• Figure 5: Per-round diagnostics: subgraph size, path count, and answer confidence.