大语言模型与知识图谱协同驱动的知识推理技术研究综述

doi:10.11896/jsjkx.251000027

Abstract

Abstract: As a cornerstone for achieving cognitive intelligence in artificial intelligence(AI),knowledge reasoning technology aims to derive novel insights from existing knowledge to enhance knowledge systems,serving as a pivotal indicator of machine intelligence.With rapid advances in AI,traditional single-mode approaches for knowledge representation and reasoning have become inadequate for addressing the sophisticated demands of complex intelligent scenarios.This challenge has catalyzed growing academic interest in collaborative reasoning frameworks that synergize explicit symbolic systems(e.g.,Knowledge Graphs(KGs)) and implicit parametric systems(e.g.,Large Language Models(LLMs)).The former employs structured triples for precise knowledge representation,while the latter leverages neural networks to encode vast semantic information.Recent years have witnessed remarkable progress in both paradigms.Notably,the emergent generalization capabilities of ChatGPT in cross-domain reasoning and DeepSeek-R1’s breakthroughs in parametric knowledge processing have laid a critical foundation for neuro-symbolic collaboration.Nevertheless,inherent limitations persist due to representational disparities:symbolic systems face constraints in knowledge coverage and dynamic update efficiency,whereas parametric systems encounter challenges such as factual hallucinations and limited interpretability in reasoning processes.The integration of LLMs and KGs has emerged as a transformative research direction,combining the semantic comprehension strengths of LLMs with the explainability advantages of KGs.This synergy enables deep logical analysis of natural language queries,effectively overcoming the constraints of conventional knowledge reasoning methods.Such advancements provide robust support for downstream AI applications,including information retrieval,intelligent question-answering,and recommendation systems,thereby fostering the development of comprehensive,reliable,and controllable AI knowledge processing frameworks.This paper presents a systematic review of knowledge reasoning from the perspective of LLM-KG collaboration.Firstly,it formalizes the conceptual framework and technical characteristics of KG-based and LLM-driven reasoning methodologies.Secondly,it synthesizes recent advancements in collaborative reasoning across three dimensions:LLM-enhanced KG reasoning,KG-augmented LLM reasoning,and interactive co-reasoning mechanisms.Finally,it critically analyzes unresolved challenges and proposes future research directions for this interdisciplinary paradigm,aiming to advance the frontiers of knowledge systems.

Key words: Knowledge reasoning, Large language model, Knowledge graph, Neuro-symbolic systems

CLC Number:

TP391

XIA Yi, ZHOU Gang, ZHANG Kaixiang, LAN Mingjing, LI Zhufeng, SU Benrong, HE Haofeng, FENG Shizhong. Survey on Collaborative-driven Knowledge Reasoning Technologies Integrating Large Language Models and Knowledge Graphs[J].Computer Science, 2026, 53(9): 38-54.

References

[1] GUIDOTTI R,MONREALE A,RUGGIERI S,et al.A Survey of Methods for Explaining Black Box Models[J].ACM Computing Surveys,2019,51(5):1-42.
[2] ZHANG Y,TIŇO P,LEONARDIS A,et al.A Survey on Neural Network Interpretability[J].IEEE Transactions on Emerging Topics in Computational Intelligence,2021,5(5):726-742.
[3] PAN S,LUO L,WANG Y,et al.Unifying Large LanguageModels and Knowledge Graphs:A Roadmap[J].IEEE Transactions on Knowledge and Data Engineering,2024,36(7):3580-3599.
[4] LECUE F.On the Role of Knowledge Graphs in Explainable AI[J].Semantic Web,2019,11(1):1-11.
[5] PUJARA J,MIAO H,GETOOR L,et al.Knowledge GraphIdentification[C] //Proceedings of the 2013 International Semantic Web Conference.2013:542-557.
[6] UDAYAKUMAR A.XAI-Explainable Artificial Intelligence[J].Science Robotics,2019,4(37):7120.
[7] YANG D,QU B,ROSSO P,et al.Fast and slow thinking:A two-step schema-aware approach for instance completion in knowledge graphs[J].IEEE Transactions on Knowledge and Data Engineering,2024,36(3):1113-1129.
[8] HU S,ZOU L,YU J X,et al.Answering Natural LanguageQuestions by Subgraph Matching over Knowledge Graphs[J].IEEE Transactions on Knowledge and Data Engineering,2018,30(5):824-837.
[9] PALUMBO E,RIZZO G,TRONCY R.Entity2rec:Learning User-Item Relatedness from Knowledge Graphs for Top-N Item Recommendation[C] //Proceedings of the 2017 ACM Conference on Recommender Systems.2017:32-36.
[10] GUAN S P,JIN X L,JIA Y T,et al.Research progress onknowledge reasoning for knowledge graphs [J].Journal of Software,2018,29(10):2966-2994.
[11] YANG L,CHEN H,LI Z,et al.Give Us the Facts:Enhancing Large Language Models with Knowledge Graphs for Fact-aware Language Modeling[J].IEEE Transactions on Knowledge and Data Engineering,2024,36(7):3091-3110.
[12] ZHU Y,WANG X,CHEN J,et al.LLMs for knowledge graph construction and reasoning:recent capabilities and future opportunities[J].World Wide Web,2024,27(5):58.
[13] CHEN X,JIA S,XIANG Y.A Review:Knowledge Reasoningover Knowledge Graph[J].Expert Systems with Applications,2020,141:112948.
[14] XIA Y,LUO J,LAN M,et al.Reason More Like Human:Incorporating Meta Information into Hierarchical Reinforcement Learning for Knowledge Graph Reasoning[J].Applied Intelligence,2022,53:13293-13308.
[15] BADER S R,GRANGEL-GONZALEZ I,NANJAPPA P,et al.A Knowledge Graph for Industry 4.0[C] //Proceedings of the 2020 European Semantic Web Conference.2020:465-480.
[16] SARHAN I,SPRUIT M.Open-CyKG:An Open Cyber Threat Intelligence Knowledge Graph[J].Knowledge-Based Systems,2021,233:107524.
[17] RUIS L,MORZES M,BAE J,et al.Procedural Knowledge in Pretraining Drives Reasoning in Large Language Models[C] //ICLR 2025.2025.
[18] PAN J Z,RAZNIEWSKI S,KALO J C,et al.Large Language Models and Knowledge Graphs:Opportunities and Challenges[J].Transactions on Graph Data and Knowledge,2023,1(1):1-38.
[19] WANG M,WANG H F,LI B H,et al.A survey on key technologies of next-generation knowledge graphs [J].Journal of Computer Research and Development,2022,59(9):1947-1965.
[20] DEHAL R S,SHARMA M,RAJABI E.Knowledge graphs and their reciprocal relationship with large language models [J].Machine Learning and Knowledge Extraction,2025,7(2):38.
[21] XU D,ZHANG Z,LIN Z,et al.Multi-perspective Improvement of Knowledge Graph Completion with Large Language Models[C] //Proceedings of the 2024 Joint International Conference on Computational Linguistics.2024:11956-11968.
[22] TRAJANOSKA M,STOJANOV R,TRAJANOV D.Enhancing Knowledge Graph Construction Using Large Language Models[J].arXiv:2305.04676,2023.
[23] PADIA A,FERRARO F,FININ T.Enhancing KnowledgeGraph Consistency through Open Large Language Models:A Case Study[C] //Proceedings of the 2025 Association for the Advancement of Artificial Intelligence.2024:203-208.
[24] VISHWAS M,PEZESHKPOUR P,HRUSCHKA E,et al.Rethinking Language Models as Symbolic Knowledge Graphs[J].arXiv:2308.13676,2023.
[25] PETRONI F,ROCKTÄSCHEL T,RIEDEL S,et al.Language Models as Knowledge Bases?[C] //Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing.2019:2463-2473.
[26] LI Q,CHEN Z,JI C,et al.LLM-based Multi-Level Knowledge Generation for Few-shot Knowledge Graph Completion[C] //Proceedings of the 2024 International Joint Conference on Artificial Intelligence.2024:2135-2143.
[27] LIU X,LIU Y,HU W.Knowledge graph error detection with contrastive confidence adaption[C] //Proceedings of the 2024 Association for the Advancement of Artificial Intelligence.2024:8824-8831.
[28] YAO L,PENG J,MAO C,et al.Exploring Large LanguageModels for Knowledge Graph Completion[J].arXiv:2308.13916,2024.
[29] WEI Y,HUANG Q,ZHANG Y,et al.KICGPT:Large Lan-guage Model with Knowledge in Context for Knowledge Graph Completion[C] //Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing.2023:8667-8683.
[30] JIANG P,AGARWAL S,JIN B,et al.Text Augmented Open Knowledge Graph Completion via Pre-Trained Language Models[C] //Findings of the 2023 Association for Computational Linguistics.2023:11161-11180.
[31] FANG H,ZHU X,GUREVYCH I.DARA:Decomposition-Alignment-Reasoning Autonomous Language Agent for Question Answering over Knowledge Graphs[C] //Findings of the 2024 Association for Computational Linguistics.2024:3406-3432.
[32] LIU L,HILL B,DU B,et al.Conversational Question Answering with Language Models Generated Reformulations over Knowledge Graph[C] //Findings of the 2024 Association for Computational Linguistics.2024:839-850.
[33] GUO T,YANG Q,WANG C,et al.KnowledgeNavigator:Leveraging Large Language Models for Enhanced Reasoning over Knowledge Graph[J].Complex & Intelligent Systems,2024,10(5):7063-7076.
[34] ZHOU D,SCHÄRLI N,HOU L,et al.Least-to-most prompting enables complex reasoning in large language models[C] //Proceedings of the 2023 International Conference on Learning Representations.2023.
[35] WANG C,XU Y,PENG Z,et al.keqing:knowledge-based question answering is a nature chain-of-thought mentor of LLM[J].arXiv:2401.00426,2023.
[36] JIANG J,ZHOU K,ZHAO X,et al.ReasoningLM:EnablingStructural Subgraph Reasoning in Pre-trained Language Models for Question Answering over Knowledge Graph[C] //Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing.2023:3721-3735.
[37] LUO H,E H,TANG Z,et al.ChatKBQA:A Generate-then-Retrieve Framework for Knowledge Base Question Answering with Fine-tuned Large Language Models[C] //Findings of the 2024 Association for Computational Linguistics.2024:2039-2056.
[38] CHEN Y,ZHANG Y,YU J,et al.In-context learning forknowledge base question answering for unmanned systems based on large language models[J].arXiv:2311.02956,2023.
[39] LI Z,DENG L,LIU H,et al.UniOQA:A Unified Framework for Knowledge Graph Question Answering with Large Language Models[J].arXiv:2406.02110,2024.
[40] HU C,FU J,DU C,et al.ChatDB:Augmenting LLMs with Databases as Their Symbolic Memory[J].arXiv:2306.03901,2023.
[41] LAN Y,JIANG J.Query Graph Generation for AnsweringMulti-hop Complex Questions from Knowledge Bases[C] //Proceedings of the 2020 Annual Meeting of the Association for Computational Linguistics.2020:969-974.
[42] ZHANG J,CAO S,HU L,et al.KB-Plugin:A Plug-and-playFramework for Large Language Models to Induce Programs over Low-resourced Knowledge Bases[C] //Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing.2024:2868-2882.
[43] GALKIN M,YUAN X,MOSTAFA H,et al.Towards foundation models for knowledge graph reasoning[C] //Proceedings of the 2024 International Conference on Learning Representations.2024.
[44] HUANG Y.Evaluating and Enhancing Large Language Models for Conversational Reasoning on Knowledge Graphs[J].arXiv:2312.11282,2024.
[45] CUI Y,SUN Z,HU W.A prompt-based knowledge graph foundation model for universal in-context reasoning[C] //Proceedings of the 2024 Advances in Neural Information Processing Systems.2024.
[46] LIN Q,ZHAO T,HE K,et al.Self-supervised Quantized Representation for Seamlessly Integrating Knowledge Graphs with Large Language Models[C] //Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics.2025:13587-13602.
[47] WANG K,XU Y,WU Z,et al.LLM as Prompter:Low-resource Inductive Reasoning on Arbitrary Knowledge Graphs[C] //Findings of the 2024 Association for Computational Linguistics.2024:3742-3759.
[48] WANG Y,HU M,HUANG Z,et al.KC-GenRe:A Knowledge-constrained Generative Re-ranking Method Based on Large Language Models for Knowledge Graph Completion[C] //Proceedings of the 2024 Joint International Conference on Computational Linguistics.2024:9668-9680.
[49] REIMERS N,GUREVYCH I.Sentence-BERT:Sentence Em-beddings using Siamese BERT-Networks[C] //Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing.2019:3980-3990.
[50] ZHANG Y,LI M,LONG D,et al.Qwen3 Embedding:Advan-cing Text Embedding and Reranking Through Foundation Models[J].arXiv:2506.05178,2025.
[51] PUJARA J,HUI M,GETOOR L,et al.Ontology-Aware Partitioning for Knowledge Graph Identification[C] //Proceedings of the Workshop on Automated Knowledge Base Construction.2013.
[52] ZOU Y,FININ T W,CHEN H.F-OWL:An Inference Engine for Semantic Web[C] //Proceedings of the International Conference on Formal Approaches to Agent-based Systems.2004:126-139.
[53] SIRIN E,PARSIA B,GRAU B C,et al.Pellet:A PracticalOWL-DL Reasoner[J].Journal of Web Semantics,2007,5(2):51-53.
[54] SHANG Z,KE W,XIU N,et al.OntoFact:Unveiling fantastic fact-skeleton of llms via ontology-driven reinforcement learning[C] //Proceedings of the 2024 Association for the Advancement of Artificial Intelligence.2024:18934-18943.
[55] WU C,KE W,WANG P,et al.ConsistNER:Towards instructive NER demonstrations for llms with the consistency of ontology and context[C] //Proceedings of the 2024 Association for the Advancement of Artificial Intelligence.2024:19234-19242.
[56] FENG X,WU X,MENG H.Ontology-grounded automaticknowledge graph construction by LLM under wikidata schema[C] //Proceedings of the KDD Workshop on Human-Interpretable AI.2024:117-135.
[57] HYVÖNEN E.How to create and use a national cross-domain ontology and data infrastructure on the Semantic Web[J].Semantic Web,2024,15(4):1499-1513.
[58] LUO L,JU J,XIONG B,et al.ChatRule:Mining Logical Rules with Large Language Models for Knowledge Graph Reasoning[M] //Advances in Knowledge Discovery and Data Mining.Singapore:Springer Nature Singapore,2025:314-325.
[59] SERVANTEZ S,BARROW J,HAMMOND K J,et al.Chain of logic:Rule-based reasoning with large language models[C] //Findings of the Association for Computational Linguistics.2024:2721-2733.
[60] XU J,FEI H,PAN L,et al.Faithful Logical Reasoning via Symbolic Chain-of-Thought[C] //Proceedings of the 2024 Annual Meeting of the Association for Computational Linguistics.2024:13326-13365.
[61] AGARWAL O,GE H,SHAKERI S,et al.Knowledge Graph Based Synthetic Corpus Generation for Knowledge-Enhanced Language Model Pre-training[C] //Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics.2021:3554-3565.
[62] YUAN S,CHEN J,SUN C,et al.ANALOGYKB:UnlockingAnalogical Reasoning of Language Models with A Million-scale Knowledge Base[C] //Proceedings of the 2024 Annual Meeting of the Association for Computational Linguistics.2024:1249-1265.
[63] CHEN W,SU Y,YAN X,et al.KGPT:Knowledge-Grounded Pre-Training for Data-to-Text Generation[C] //Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing.2020:8635-8648.
[64] SHEN T,MAO Y,HE P,et al.Exploiting Structured Know-ledge in Text via Graph-Guided Representation Learning[C] //Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing.2020:8980-8994.
[65] WANG X,GAO T,ZHU Z,et al.KEPLER:A unified model for knowledge embedding and pre-trained language representation[J].Transactions on the Association for Computational Linguistics,2021,9:176-194.
[66] ZHANG Y,CHEN Z,FANG Y,et al.Knowledgeable Prefe-rence Alignment for LLMs in Domain-specific Question Answering[C] //Findings of the 2024 Association for Computational Linguistics,2024:891-904.
[67] CHOUDHARY N,REDDY C K.Complex Logical Reasoningover Knowledge Graphs using Large Language Models[J].arXiv:2305.01157,2024.
[68] TIAN S,LUO Y,XU T,et al.KG-Adapter:Enabling Knowledge Graph Integration in Large Language Models through Parameter-Efficient Fine-Tuning[C] //Findings of the 2024 Association for Computational Linguistics.2024:3813-3828.
[69] BAEK J,AJI A F,SAFFARI A.Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answering[J].arXiv:2306.04136,2023.
[70] ZHANG J,ZHANG X,YU J,et al.Subgraph Retrieval En-hanced Model for Multi-hop Knowledge Base Question Answering[C] //Proceedings of the 2022 Annual Meeting of the Association for Computational Linguistics.2022:5773-5784.
[71] SHU Y,YU Z,LI Y,et al.TIARA:Multi-grained Retrieval for Robust Question Answering over Large Knowledge Base[C] //Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing.2022:8108-8121.
[72] LIU H,WANG S,ZHU Y,et al.Knowledge Graph-EnhancedLarge Language Models via Path Selection[C] //Findings of the 2024 Association for Computational Linguistics.2024:6311-6321.
[73] ZHANG Y,CHEN Z,GUO L,et al.Making Large Language Models Perform Better in Knowledge Graph Completion[C] //Proceedings of 2024 International Conference on Multimedia.2024:233-242.
[74] YE X,YAVUZ S,HASKIMOTO K,et al.RNG-KBQA:Generation Augmented Iterative Ranking for Knowledge Base Question Answering[C] //Proceedings of the 2022 Annual Meeting of the Association for Computational Linguistics.2022:6032-6043.
[75] MAVROMATIS C,KARYPIS G.GNN-RAG:Graph NeuralRetrieval for Large Language Model Reasoning[J].arXiv:2405.20139,2024.
[76] YASUNAGA M,BOSSELUT A,REN H,et al.Deep Bidirectional Language-Knowledge Graph Pretraining[C] //Proceedings of the 2022 Advances in Neural Information Processing Systems.2022:37309-37323.
[77] TIAN Y,SONG H,WANG Z,et al.Graph Neural Promptingwith Large Language Models[J].Proceedings of the AAAI Conference on Artificial Intelligence,2024,38(17):19080-19088.
[78] LIU H,WANG S,CHEN C,et al.Question-Aware Knowledge Graph Prompting for Enhancing Large Language Models[C] //Findings of the Association for Computational Linguistics.2025:1388-1400.
[79] DARREN E,HA T,DAYENNE S.GraphRAG:A NovelFramework for Global Reasoning Retrieval-Augmented Generation[EB/OL].https://graphrag.homes/.
[80] GUTIERREZ B J,SHU Y,GU Y,et al.HippoRAG:Neurobiologically inspired long-term memory for large language models[C] //NeurIPS 2024.2024.
[81] GUTIÉRREZ B J,SHU Y,QI W,et al.From RAG to Memory:Non-Parametric Continual Learning for Large Language Models[J].arXiv:2502.14802,2025.
[82] LI M,MIAO S,LI P.Simple is effective:The roles of graphs and large language models in knowledge-graph-based retrieval-augmented generation[C] //The Thirteenth International Conference on Learning Representations.2025.
[83] GUO Z,XIA L,YU Y,et al.LightRAG:Simple and fast retrie-val-augmented generation[C] //Findings of the Association for Computational Linguistics:EMNLP 2025.2025:10746-10761.
[84] CHEN B,GUO Z,YANG Z,et al.PathRAG:Pruning graph-based retrieval augmented generation with relational paths[C] //Proceedings of the 40^th Annual AAAI Conference on Artificial Intelligence.2026:30183-30191.
[85] YU D,ZHANG S,NG P,et al.DecAF:Joint Decoding of Answers and Logical Forms for Question Answering over Knowledge Bases[C] //Proceedings of the 2023 International Conference on Learning Representations.2023.
[86] JIANG J,ZHOU K,ZHAO X,et al.UniKGQA:Unified Retrieval and Reasoning for Solving Multi-hop Question Answering Over Knowledge Graph[C] //Proceedings of the 2023 International Conference on Learning Representations.2023.
[87] TAN X,WANG X,LIU Q,et al.Paths-over-graph:Knowledge graph empowered large language model reasoning[C] //Proceedings of the ACM on Web Conference 2025.2025:3505-3522.
[88] AGARWAL D,DAS R,KHOSLA S,et al.Bring Your OwnKG:Self-Supervised Program Synthesis for Zero-Shot KGQA[C] //Findings of the 2024 Association for Computational Linguistics.2024:896-919.
[89] WANG J,SUN Q,LI X,et al.Boosting Language Models Reasoning with Chain-of-Knowledge Prompting[C] //Proceedings of the 2024 Annual Meeting of the Association for Computational Linguistics.2024:4958-4981.
[90] KIM J,KWON Y,JO Y,et al.KG-GPT:A General Framework for Reasoning on Knowledge Graphs Using Large Language Models[C] //Findings of the 2023 Association forComputationalLinguistics.2023:9410-9421.
[91] CHEN Z,CHEN J,CHEN Y,et al.LMExplainer:GroundingKnowledge and Explaining Language Models[J].arXiv:2303.16537,2024.
[92] WU Y,HAN X,SONG W,et al.MindMap:Constructing Evidence Chains for Multi-Step Reasoning in Large Language Models[J].Proceedings of the 2024 Association for the Advancement of Artificial Intelligence,2024,38(17):19270-19278.
[93] CHEN Z,CHEN J,SINGH A,et al.XplainLLM:A Knowledge-Augmented Dataset for Reliable Grounded Explanations in LLMs[C] //Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing.2024:7578-7596.
[94] PANDA P,AGARWAL A,DEVAGUPTAPU C,et al.HOLMES:Hyper-Relational Knowledge Graphs for Multi-hop Question Answering using LLMs[C] //Proceedings of the 2024 Annual Meeting of the Association for Computational Linguistics.2024:13263-13282.
[95] LAO N,MITCHELL T M,COHEN W W.Random Walk Inference and Learning in A Large Scale Knowledge Base[C] //Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing.2011:2815-2824.
[96] LIN X,SOCHER R XIONG C M.Multi-Hop Knowledge Graph Reasoning with Reward Shaping[C] //Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing.2019:3243-3253.
[97] LI Z,JIN X,GUAN S,et al.Path Reasoning over Knowledge Graph:A Multi-agent and Reinforcement Learning Based Method[C] //Proceedings of the 2018 IEEE International Conference on Data Mining Workshops.2018:929-936.
[98] SUN J,XU C,TANG L,et al.Think-on-Graph:Deep and Responsible Reasoning of Large Language Model on Knowledge Graph[C] //Proceedings of the 2024 International Conference on Learning Representations.2024.
[99] MA S,XU C,JIANG X,et al.Think-on-graph 2.0:Deep and faithful large language model reasoning with knowledge-guided retrieval augmented generation[C] //The Thirteenth International Conference on Learning Representations.2025.
[100] LUO L,LI Y F,HAFFARI G,et al.Reasoning on Graphs:Faithful and Interpretable Large Language Model Reasoning[C] //Proceedings of the 2024 International Conference on Learning Representations.2024.
[101] LIU G,ZHANG Y,LI Y,et al.Explore then Determine:AGNN-LLM Synergy Framework for Reasoning over Knowledge Graph[J].arXiv:2406.01145,2024.
[102] XU Y,HE S,CHEN J,et al.Generate-on-Graph:Treat LLM as both Agent and KG in Incomplete Knowledge Graph Question Answering[J].arXiv:2404.14741,2024.
[103] HAO S,GU Y,MA H,et al.Reasoning with language model is planning with world model[C] //Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing.2023:8154-8173.
[104] CHENG S,ZHUANG Z,XU Y,et al.Call me when necessary:LLMs can efficiently and faithfully reason over structured environments[C] //Findings of the Association for Computational Linguistics.2024:4275-4295.
[105] WU X D,HUANG M Z,BU C Y.BEKO:Bidirectional enhancement between large language models and knowledge graphs [J].Chinese Journal of Computers,2025,48(07):1572-1588.
[106] DI MAURO A,XU Z,BEN RIM W,et al.Generating and Evaluating Plausible Explanations for Knowledge Graph Completion[C] //Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics.2024:12106-12118.
[107] JIANG J,ZHOU K,DONG Z,et al.StructGPT:A GeneralFramework for Large Language Model to Reason over Structured Data[C] //Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing.2023:9237-9251.
[108] JIANG J,ZHOU K,ZHAO X,et al.KG-Agent:An EfficientAutonomous Agent Framework for Complex Reasoning over Knowledge Graph[C] //Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics.2025:9505-9523.
[109] SCHICK T,DWIVEDI-YU J,DESSI R,et al.Toolformer:Language models can teach themselves to use tools[C] //Proceedings of the 2023 Advances in Neural Information Processing Systems.2023,36:68539-68551.
[110] ZHANG J.Graph-ToolFormer:To Empower LLMs with Graph Reasoning Ability via Prompt Augmented by ChatGPT[J].ar-Xiv:2304.11116,2023.
[111] WANG X,YANG Q,QIU Y,et al.KnowledGPT:EnhancingLarge Language Models with Retrieval and Storage Access on Knowledge Bases[J].arXiv:2308.11761,2023.
[112] ZHU Y,QIAO S,OU Y,et al.KnowAgent:Knowledge-Augmented Planning for LLM-Based Agents[J].arXiv:2403.03101,2024.
[113] TANG X,QIN T,PENG T,et al.Agent KB:leveraging cross-domain experience for agentic problem solving[J].arXiv:2507.06229,2025.
[114] LUO H,E H,GUO Y,et al.KBQA-o1:Agentic knowledge base question answering with monte carlo tree search[J].arXiv:2501.18922,2025.
[115] MARKOWITZ E,RAMAKRISHNA A,DHAMALA J,et al.Tree-of-Traversals:A Zero-Shot Reasoning Algorithm for Augmenting Black-box Language Models with Knowledge Graphs[C] //Proceedings of the 2024 Annual Meeting of the Association for Computational Linguistics.2024:12302-12319.
[116] SUN L,TAO Z,LI Y,et al.ODA:observation-driven agent for integrating llms and knowledge graphs[C] //Findings of the Association for Computational Linguistics.2024:7417-7431.
[117] CHEN L,TONG P,JIN Z,et al.Plan-on-graph:Self-correcting adaptive planning of large language model on knowledge graphs[C] //NeurIPS 2024.2024.
[118] DONG Z,PENG B,WANG Y,et al.EffiQA:Efficient question-answering with strategic multi-model collaboration on know-ledge graphs[C] //Proceedings of the 31st International Conference on Computational Linguistics.2025:7180-7194.
[119] JIN B,ZENG H,YUE Z,et al.Search-R1:Training llms to reason and leverage search engines with reinforcement learning[J].arXiv:2503.09516,2025.
[120] ZHANG D,XU J,ZHOU J,et al.KAG-Thinker:InteractiveThinking and Deep Reasoning in LLMs via Knowledge-Augmented Generation[J].arXiv:2506.17728,2025.

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