mlsanityData to 14 Oct 2026

Mixture of Structural-and-Textual Retrieval over Text-rich Graph Knowledge Bases

arXiv 2502.20317 ↗First posted 27 Feb 2025cs.LG cs.AI cs.IRcited by 11

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Abstract (arXiv)

Text-rich Graph Knowledge Bases (TG-KBs) have become increasingly crucial for answering queries by providing textual and structural knowledge. However, current retrieval methods often retrieve these two types of knowledge in isolation without considering their mutual reinforcement and some hybrid methods even bypass structural retrieval entirely after neighboring aggregation. To fill in this gap, we propose a Mixture of Structural-and-Textual Retrieval (MoR) to retrieve these two types of knowledge via a Planning-Reasoning-Organizing framework. In the Planning stage, MoR generates textual planning graphs delineating the logic for answering queries. Following planning graphs, in the Reasoning stage, MoR interweaves structural traversal and textual matching to obtain candidates from TG-KBs. In the Organizing stage, MoR further reranks fetched candidates based on their structural trajectory. Extensive experiments demonstrate the superiority of MoR in harmonizing structural and textual retrieval with insights, including uneven retrieving performance across different query logics and the benefits of integrating structural trajectories for candidate reranking. Our code is available at https://github.com/Yoega/MoR.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 32 of its 52 references that are in the map. First posted on arXiv 27 Feb 2025.

Builds on

32 of 52 references.

References · 32

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,824
  3. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,212
  4. 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarizationcited by 1,453
  5. 2021Unsupervised Dense Information Retrieval with Contrastive Learningcited by 1,216
  6. 2024The AI Scientist: Towards Fully Automated Open-Ended Scientific Discoverycited by 1,071
  7. 2017Reading Wikipedia to Answer Open-Domain Questionscited by 777
  8. 2018GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Modelscited by 413
  9. 2021QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answeringcited by 388
  10. 2023StructGPT: A General Framework for Large Language Model to Reason over Structured Datacited by 343
  11. 2023Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoningcited by 328
  12. 2023Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender Systemcited by 242
20 more
  1. 2023Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge Graphcited by 238
  2. 2020Generation-Augmented Retrieval for Open-domain Question Answeringcited by 186
  3. 2023LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examplescited by 177
  4. 2024Retrieval-Augmented Generation with Graphs (GraphRAG)cited by 172
  5. 2022GreaseLM: Graph REASoning Enhanced Language Models for Question Answeringcited by 171
  6. 2019End-to-End Open-Domain Question Answering with BERTserinicited by 152
  7. 2023Knowledge Graph Prompting for Multi-Document Question Answeringcited by 152
  8. 2024GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoningcited by 128
  9. 2024Graph Chain-of-Thought: Augmenting Large Language Models by Reasoning on Graphscited by 119
  10. 2024AvaTaR: Optimizing LLM Agents for Tool Usage via Contrastive Reasoningcited by 72
  11. 2022A Survey on Deep Graph Generation: Methods and Applicationscited by 69
  12. 2024STaRK: Benchmarking LLM Retrieval on Textual and Relational Knowledge Basescited by 55
  13. 2024Tag-LLM: Repurposing General-Purpose LLMs for Specialized Domainscited by 30
  14. 2025Towards Trustworthy Retrieval Augmented Generation for Large Language Models: A Surveycited by 30
  15. 2024HybGRAG: Hybrid Retrieval-Augmented Generation on Textual and Relational Knowledge Basescited by 29
  16. 2024Knowledge-Aware Query Expansion with Large Language Models for Textual and Relational Retrievalcited by 26
  17. 2024Graph-Based Retriever Captures the Long Tail of Biomedical Knowledgecited by 20
  18. 2023GrapeQA: GRaph Augmentation and Pruning to Enhance Question-Answeringcited by 17
  19. 2024Federated Recommendation via Hybrid Retrieval Augmented Generationcited by 16
  20. 2019Retrieve, Read, Rerank: Towards End-to-End Multi-Document Reading Comprehensioncited by 15

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 4; 2026: 7.

11 papers.

  1. 2025Can Knowledge-Graph-based Retrieval Augmented Generation Really Retrieve What You Need?cited by 8
  2. 2025GraphRAFT: Retrieval Augmented Fine-Tuning for Knowledge Graphs on Graph Databasescited by 7
  3. 2026GRASP: Plan-Guided Graph Retrieval with Adaptive Fusion and Reranking on Semi-Structured Knowledge Basescited by 7
  4. 2025Reasoning by Exploration: A Unified Approach to Retrieval and Generation over Graphscited by 6
  5. 2025Autofocus Retrieval: An Effective Pipeline for Multi-Hop Question Answering With Semi-Structured Knowledgecited by 3
  6. 2026Autonomous Knowledge Graph Exploration with Adaptive Breadth-Depth Retrievalcited by 3
  7. 2026RL-Index: Reinforcement Learning for Retrieval Index Reasoningcited by 2
  8. 2026SAGE: Structure Aware Graph Expansion for Retrieval of Heterogeneous Datacited by 0
  9. 2026Cognition on Graph: Navigating Massive Knowledge Space via Cognitive Cycles and Bidirectional Graph-Text Synergycited by 0
  10. 2026Co-adaptive Rule Routing and Constraining for Structured Knowledge Retrievalcited by 0
  11. 2026Knowledge Graphs as Files: An Agent Harness Where Tool Creation and Tool Use Co-Evolvecited by 0