---
title: "Query-Aware Graph Attention for Precise Subgraph Retrieval in"
description: "The authors propose QSRAG, a two-stage pipeline. 36 references. Among them: 0."
authors: ["Yuanye Xu", "Linyi Guo", "Yue Zhang", "Fu Ning"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: ACL 2026"
source: "https://aclanthology.org/2026.findings-acl.398/"
doi: "10.18653/v1/2026.findings-acl.398"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/xu-2026-query-aware-graph-attention-precise/"
---
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# Query-Aware Graph Attention for Precise Subgraph Retrieval in Knowledge-Augmented Reasoning

**Authors:** Yuanye Xu, Linyi Guo, [Yue Zhang](https://md.research.elamur.ai/free/en/authors/yue-zhang/) and Fu Ning · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: ACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.findings-acl.398/) · [PDF](https://aclanthology.org/2026.findings-acl.398.pdf) · [DOI 10.18653/v1/2026.findings-acl.398](https://doi.org/10.18653/v1/2026.findings-acl.398) · [OpenAlex](https://openalex.org/W7166812376)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Graph-based retrieval for LLM question answering often returns evidence that looks plausible but is wrong, because current retrievers score knowledge-graph triples without linking the question's meaning to the relation type of each edge. For "Where was Rihanna raised?", SubgraphRAG returns Barbados, not Saint Michael Parish, because it cannot separate "born" from "raised". The authors propose QSRAG, a two-stage pipeline. Its core is QR-GAT, a graph attention network that feeds question semantics and relation embeddings into the attention step of message passing. It also uses the query to initialize nodes and to compute the final triple scores. The top-scoring triples form a compact subgraph, which one LLM call then reasons over through in-context learning, with no fine-tuning. Tests on WebQSP and CWQ (both Freebase) report state-of-the-art Triple Recall and Answer Recall and stronger LLM answer accuracy. The work shows that careful, cheap structured retrieval can replace pipelines that call the LLM many times.

## Key points
- Entities, relations and questions are embedded with Qwen3-Embedding-0.6B. Final reasoning takes only one LLM inference. Subgraphs are built by top-k selection or probability-mass (top-p) filtering.
- Ablation: the full QR-GAT reaches Triple Recall of 0.906 on WebQSP and 0.914 on CWQ.
  - Removing the query from node initialization drops WebQSP to 0.819.
  - Removing it from final scoring drops CWQ to 0.851.
  - Removing it from attention costs about 5.4% on WebQSP and 3.7% on CWQ.
- Recall keeps rising as more triples are retrieved. At k=500, Triple Recall is 0.979 on WebQSP and 0.987 on CWQ.
- Downstream accuracy with Llama-3.1-8B-Instruct peaks at about 100–200 triples. Examples:
  - WebQSP at k=100: Macro-F1 70.61, Hit 85.63.
  - CWQ at k=200: Hit 57.18.
  - Adding more triples beyond this lowers accuracy.
- Stated limitation: there is no re-ranking, denoising or other post-retrieval evidence processing, so some noisy triples still reach the LLM.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Northwestern Polytechnical University](https://md.research.elamur.ai/free/en/universities/northwestern-polytechnical-university/) — University, Xi'an, China · 15 works · 67 citations
- [San Francisco Bay University](https://md.research.elamur.ai/free/en/universities/san-francisco-bay-university/) — University, Fremont, United States · 2 works

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## Cite

Cite as: Yuanye Xu, Linyi Guo, Yue Zhang and Fu Ning. 2026. Query-Aware Graph Attention for Precise Subgraph Retrieval in Knowledge-Augmented Reasoning. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{xu2026query,
  title = {{Query-Aware Graph Attention for Precise Subgraph Retrieval in Knowledge-Augmented Reasoning}},
  author = {Yuanye Xu, Linyi Guo, Yue Zhang and Fu Ning},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.398},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.398}
}
```

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*Updated 2 November 2026*