Query-Specific GNN: A Comprehensive Graph Representation Learning Method for Retrieval Augmented Generation

• URL: http://arxiv.org/abs/2510.11541v1
• Date: Mon, 13 Oct 2025 15:41:15 GMT
• Title: Query-Specific GNN: A Comprehensive Graph Representation Learning Method for Retrieval Augmented Generation
• Authors: Yuchen Yan, Zhihua Liu, Hao Wang, Weiming Li, Xiaoshuai Hao,
• Abstract summary: Multi-hop questions require the identification of multiple knowledge targets to form a synthesized answer.<n>Existing methods often struggle to fully understand the questions with complex semantic structures.<n>We propose a novel graph representation learning framework for multi-hop question retrieval.
• Score: 23.133432599408327
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Retrieval-augmented generation (RAG) has demonstrated its ability to enhance Large Language Models (LLMs) by integrating external knowledge sources. However, multi-hop questions, which require the identification of multiple knowledge targets to form a synthesized answer, raise new challenges for RAG systems. Under the multi-hop settings, existing methods often struggle to fully understand the questions with complex semantic structures and are susceptible to irrelevant noise during the retrieval of multiple information targets. To address these limitations, we propose a novel graph representation learning framework for multi-hop question retrieval. We first introduce a Multi-information Level Knowledge Graph (Multi-L KG) to model various information levels for a more comprehensive understanding of multi-hop questions. Based on this, we design a Query-Specific Graph Neural Network (QSGNN) for representation learning on the Multi-L KG. QSGNN employs intra/inter-level message passing mechanisms, and in each message passing the information aggregation is guided by the query, which not only facilitates multi-granular information aggregation but also significantly reduces the impact of noise. To enhance its ability to learn robust representations, we further propose two synthesized data generation strategies for pre-training the QSGNN. Extensive experimental results demonstrate the effectiveness of our framework in multi-hop scenarios, especially in high-hop questions the improvement can reach 33.8\%. The code is available at: https://github.com/Jerry2398/QSGNN.

Related papers

• PGDA-KGQA: A Prompt-Guided Generative Framework with Multiple Data Augmentation Strategies for Knowledge Graph Question Answering [3.8623708225544755]
  Knowledge Graph Question Answering (KGQA) is a crucial task in natural language processing.<n>We propose PGDA-KGQA, a prompt-guided generative framework with multiple data augmentation strategies for KGQA.<n> Experiments demonstrate that PGDA-KGQA outperforms state-of-the-art methods on standard KGQA.
  arXiv  Detail & Related papers  (2025-06-11T05:56:03Z)
• iQUEST: An Iterative Question-Guided Framework for Knowledge Base Question Answering [6.4524748618007415]
  iQUEST is a question-guided KBQA framework that iteratively decomposes complex queries into simpler sub-questions.<n>We integrate a Graph Neural Network (GNN) to look ahead and incorporate 2-hop neighbor information at each reasoning step.
  arXiv  Detail & Related papers  (2025-06-02T15:30:02Z)
• Divide by Question, Conquer by Agent: SPLIT-RAG with Question-Driven Graph Partitioning [62.640169289390535]
  SPLIT-RAG is a multi-agent RAG framework that addresses the limitations with question-driven semantic graph partitioning and collaborative subgraph retrieval.<n>The innovative framework first create Semantic Partitioning of Linked Information, then use the Type-Specialized knowledge base to achieve Multi-Agent RAG.<n>The attribute-aware graph segmentation manages to divide knowledge graphs into semantically coherent subgraphs, ensuring subgraphs align with different query types.<n>A hierarchical merging module resolves inconsistencies across subgraph-derived answers through logical verifications.
  arXiv  Detail & Related papers  (2025-05-20T06:44:34Z)
• Knowledge Graph-extended Retrieval Augmented Generation for Question Answering [10.49712834719005]
  This paper proposes a system that integrates Large Language Models (LLMs) and Knowledge Graphs (KGs) without requiring training.<n>The resulting approach can be classified as a specific form of a Retrieval Augmented Generation (RAG) with a KG.<n>It includes a question decomposition module to enhance multi-hop information retrieval and answerability.
  arXiv  Detail & Related papers  (2025-04-11T18:03:02Z)
• Question-Aware Knowledge Graph Prompting for Enhancing Large Language Models [51.47994645529258]
  We propose Question-Aware Knowledge Graph Prompting (QAP), which incorporates question embeddings into GNN aggregation to dynamically assess KG relevance.<n> Experimental results demonstrate that QAP outperforms state-of-the-art methods across multiple datasets, highlighting its effectiveness.
  arXiv  Detail & Related papers  (2025-03-30T17:09:11Z)
• Learning Federated Neural Graph Databases for Answering Complex Queries from Distributed Knowledge Graphs [53.03085605769093]
  We propose to learn Federated Neural Graph DataBase (FedNGDB), a pioneering systematic framework that empowers privacy-preserving reasoning over multi-source graph data.<n>FedNGDB leverages federated learning to collaboratively learn graph representations across multiple sources, enriching relationships between entities, and improving the overall quality of graph data.
  arXiv  Detail & Related papers  (2024-02-22T14:57:44Z)
• Context Matters: Pushing the Boundaries of Open-Ended Answer Generation with Graph-Structured Knowledge Context [4.1229332722825]
  This paper introduces a novel framework that combines graph-driven context retrieval in conjunction to knowledge graphs based enhancement. We conduct experiments on various Large Language Models (LLMs) with different parameter sizes to evaluate their ability to ground knowledge and determine factual accuracy in answers to open-ended questions. Our methodology GraphContextGen consistently outperforms dominant text-based retrieval systems, demonstrating its robustness and adaptability to a larger number of use cases.
  arXiv  Detail & Related papers  (2024-01-23T11:25:34Z)
• ReasoningLM: Enabling Structural Subgraph Reasoning in Pre-trained Language Models for Question Answering over Knowledge Graph [142.42275983201978]
  We propose a subgraph-aware self-attention mechanism to imitate the GNN for performing structured reasoning. We also adopt an adaptation tuning strategy to adapt the model parameters with 20,000 subgraphs with synthesized questions. Experiments show that ReasoningLM surpasses state-of-the-art models by a large margin, even with fewer updated parameters and less training data.
  arXiv  Detail & Related papers  (2023-12-30T07:18:54Z)
• Logical Message Passing Networks with One-hop Inference on Atomic Formulas [57.47174363091452]
  We propose a framework for complex query answering that decomposes the Knowledge Graph embeddings from neural set operators. On top of the query graph, we propose the Logical Message Passing Neural Network (LMPNN) that connects the local one-hop inferences on atomic formulas to the global logical reasoning. Our approach yields the new state-of-the-art neural CQA model.
  arXiv  Detail & Related papers  (2023-01-21T02:34:06Z)
• UniKGQA: Unified Retrieval and Reasoning for Solving Multi-hop Question Answering Over Knowledge Graph [89.98762327725112]
  Multi-hop Question Answering over Knowledge Graph(KGQA) aims to find the answer entities that are multiple hops away from the topic entities mentioned in a natural language question. We propose UniKGQA, a novel approach for multi-hop KGQA task, by unifying retrieval and reasoning in both model architecture and parameter learning.
  arXiv  Detail & Related papers  (2022-12-02T04:08:09Z)
• Dynamic Semantic Graph Construction and Reasoning for Explainable Multi-hop Science Question Answering [50.546622625151926]
  We propose a new framework to exploit more valid facts while obtaining explainability for multi-hop QA. Our framework contains three new ideas: (a) tt AMR-SG, an AMR-based Semantic Graph, constructed by candidate fact AMRs to uncover any hop relations among question, answer and multiple facts, (b) a novel path-based fact analytics approach exploiting tt AMR-SG to extract active facts from a large fact pool to answer questions, and (c) a fact-level relation modeling leveraging graph convolution network (GCN) to guide the reasoning process.
  arXiv  Detail & Related papers  (2021-05-25T09:14:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.