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

arXiv cs.CL / 4/27/2026

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Key Points

The paper introduces STEM, a framework for knowledge-graph question answering that improves multi-hop retrieval by using global structural awareness rather than only local reasoning paths.
STEM converts queries into atomic relational assertions via a Semantic-to-Structural Projection pipeline, building an adaptive schema-guided query graph to reduce semantic mismatch.
It then performs globally-aware node anchoring and subgraph retrieval to construct an evidence reasoning graph from the knowledge graph.
A Triple-Dependent GNN (Triple-GNN) is used to generate a Global Guidance Subgraph (Guidance Graph), which helps incorporate global KG structure during graph construction.
Experiments report improved accuracy and evidence completeness for multi-hop reasoning and state-of-the-art results across multiple multi-hop benchmarks.

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.