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

📰 ArXiv cs.AI

S-Path-RAG is a semantic-aware framework for multi-hop knowledge graph question answering

advanced Published 26 Mar 2026

Action Steps

Enumerate bounded-length candidate paths using a hybrid strategy
Learn a differentiable path scorer with a contrastive path loss function
Apply the framework to multi-hop question answering tasks over large knowledge graphs
Evaluate the performance of S-Path-RAG using metrics such as accuracy and F1-score

Who Needs to Know This

AI engineers and researchers on a team can benefit from S-Path-RAG to improve question answering over large knowledge graphs, while data scientists can apply the framework to real-world problems

Key Insight

💡 S-Path-RAG improves multi-hop question answering by enumerating semantically weighted candidate paths

Key Takeaways

S-Path-RAG is a semantic-aware framework for multi-hop knowledge graph question answering

Full Article

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

Abstract:
arXiv:2603.23512v1 Announce Type: cross 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

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