Structure Guided Retrieval-Augmented Generation for Factual Queries

2026-04-21

AI Summary

This work addresses the limitations of existing retrieval-augmented generation (RAG) approaches, which rely on vector similarity retrieval and are thus susceptible to semantic noise, often failing to satisfy the multi-constraint requirements of factual queries. To overcome this, the authors propose Structure-Guided Retrieval-Augmented Generation (SG-RAG), a novel framework that formalizes the “Exact Retrieval Problem” (ERP) and reframes retrieval as an embedding-based subgraph matching task. By explicitly integrating the topological structure of knowledge graphs with semantic embeddings, SG-RAG guides large language models to generate responses that strictly adhere to all query constraints. Experiments on the newly constructed large-scale ERQA dataset demonstrate that SG-RAG substantially outperforms strong baselines, achieving absolute gains of 20.68–50.88 points on key metrics while maintaining reasonable computational overhead.

Technology Category

• Knowledge Representation and Reasoning: Knowledge Acquisition
• Machine Learning: Structured Learning
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs

Abstract

Retrieval-Augmented Generation (RAG) has been proposed to mitigate hallucinations in large language models (LLMs), where generated outputs may be factually incorrect. However, existing RAG approaches predominantly rely on vector similarity for retrieval, which is prone to semantic noise and fails to ensure that generated responses fully satisfy the complex conditions specified by factual queries, often leading to incorrect answers. To address this challenge, we introduce a novel research problem, named Exact Retrieval Problem (ERP). To the best of our knowledge, this is the first problem formulation that explicitly incorporates structural information into RAG for factual questions to satisfy all query conditions. For this novel problem, we propose Structure Guided Retrieval-Augmented Generation (SG-RAG), which models the retrieval process as an embedding-based subgraph matching task, and uses the retrieved topological structures to guide the LLM to generate answers that meet all specified query conditions. To facilitate evaluation of ERP, we construct and publicly release Exact Retrieval Question Answering (ERQA), a large-scale dataset comprising 120000 fact-oriented QA pairs, each involving complex conditions, spanning 20 diverse domains. The experimental results demonstrate that SG-RAG significantly outperforms strong baselines on ERQA, delivering absolute improvements from 20.68 to 50.88 points across all evaluation metrics, while maintaining reasonable computational overhead.

Problem

• Retrieval-Augmented Generation
• Exact Retrieval Problem
• Factual Queries
• Structural Information
• Hallucination Mitigation

Innovation

• Structure Guided RAG
• Exact Retrieval Problem
• Subgraph Matching
• Factual Query Answering
• Retrieval-Augmented Generation