STEM: Structure-Tracing Evidence Mining for Knowledge Graphs-Driven Retrieval-Augmented Generation

2026-04-24

AI Summary

This work addresses the semantic mismatch caused by structural heterogeneity in knowledge graph question answering and the lack of global structural awareness in existing methods. It reframes multi-hop reasoning as a schema-guided graph search task, constructing an adaptive query schema graph via a semantic-structure projection mechanism. By integrating a Triple-Dependent Graph Neural Network, the approach enables globally guided node anchoring and subgraph retrieval, thereby incorporating global structural information into the retrieval phase for the first time to generate high-quality evidence reasoning graphs. This strategy substantially improves both retrieval accuracy and evidence completeness of multi-hop reasoning paths, achieving state-of-the-art performance across multiple benchmark datasets.

Technology Category

• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Data Mining & Knowledge Management: Intelligent Query Processing
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• 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: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Knowledge Graph-based Question Answering (KGQA) plays a pivotal role in complex reasoning tasks but remains constrained by two persistent challenges: the structural heterogeneity of Knowledge Graphs(KGs) often leads to semantic mismatch during retrieval, while existing reasoning path retrieval methods lack a global structural perspective. To address these issues, we propose Structure-Tracing Evidence Mining (STEM), a novel framework that reframes multi-hop reasoning as a schema-guided graph search task. First, we design a Semantic-to-Structural Projection pipeline that leverages KG structural priors to decompose queries into atomic relational assertions and construct an adaptive query schema graph. Subsequently, we execute globally-aware node anchoring and subgraph retrieval to obtain the final evidence reasoning graph from KG. To more effectively integrate global structural information during the graph construction process, we design a Triple-Dependent GNN (Triple-GNN) to generate a Global Guidance Subgraph (Guidance Graph) that guides the construction. STEM significantly improves both the accuracy and evidence completeness of multi-hop reasoning graph retrieval, and achieves State-of-the-Art performance on multiple multi-hop benchmarks.

Problem

• Knowledge Graph
• Semantic Mismatch
• Structural Heterogeneity
• Multi-hop Reasoning
• Reasoning Path Retrieval

Innovation

• Structure-Tracing Evidence Mining
• Semantic-to-Structural Projection
• Triple-Dependent GNN
• Guidance Graph
• Schema-Guided Graph Search