Computer Science ›› 2026, Vol. 53 ›› Issue (2): 273-288.doi: 10.11896/jsjkx.250400033

• Artificial Intelligence • Previous Articles     Next Articles

Survey on Complex Logical Query Methods in Knowledge Graphs

CHEN Yuyin, LI Guanfeng, QIN Jing, XIAO Yuhang   

  1. School of Information Engineering,Ningxia University,Yinchuan 750021,China
    Ningxia Key Laboratory of Artificial Intelligence and Information Security for Channeling Computing Resources from the East to the West, Yinchuan 750021,China
  • Received:2025-04-08 Revised:2025-09-11 Published:2026-02-10
  • About author:CHEN Yuyin,born in 1999,postgra-duate,is a member of CCF(No.Y8937G).His main research interest is knowledge graph and reasoning.
    LI Guanfeng,born in 1979,Ph.D,associate professor,is a member of CCF(No.A0519M).His main research interests include knowledge engineering and intelligent computing.
  • Supported by:
    Key Research and Development Program of Ningxia(2023BSB03066),Natural Science Foundation of Ningxia(2024AAC03098) and National Natural Science Foundation of China(62066038).

Abstract: CLQA as a technique for deeply mining the underlying logical relationships within knowledge graphs,aims to accurately respond to complex queries through reasoning from existing facts.This technology occupies an important position in the field of knowledge graph research and has demonstrated significant advantages in various application scenarios such as semantic search and recommendation systems,effectively promoting the widespread application and in-depth development of knowledge graphs in the field of artificial intelligence.However,current research on complex logic query techniques is still scattered,especially a systematic review of integrating large language models is particularly lacking.In light of this,this paper delves into complex logical querying techniques encompassing four major categories:geometric objects,probability distributions,fuzzy logic,and large language models.It comprehensively reviews existing models and systematically summarizes the typical datasets and evaluation me-trics employed by these methods.Building on this foundation,the paper further analyzes the strengths and limitations of each method,aiming to provide comprehensive and in-depth insights into the development of complex logical querying techniques.Finally,the paper identifies the challenges currently faced by complex logical querying technologies and discusses potential research directions,offering valuable insights for future technological innovation and development.

Key words: Complex logical querying answering(CLQA), Knowledge graph(KG), Inference, Large language model

CLC Number: 

  • TP391.1
[1]GAO J T,LI Z H,LIU W J.A Strategy of Efficient and Accurate Cardinality Estimation Based on Query Result[J].Journal of Northwestern Polytechnical University,2018,36(4):768-777.
[2]JIA S,LIU X,ZHAO P,et al.Representation of job-skill in artificial intelligence with knowledge graph analysis[C]//2018 IEEE Symposium on Product Compliance Engineering-Asia(ISPCE-CN).IEEE,2018:1-6.
[3]REN H,GALKIN M,COCHEZ M,et al.Neural Graph Reasoning:Complex Logical Query Answering Meets Graph Databases[J].arXiv:2303.14617,2023.
[4]FENG T Y,LI W P,GUO Q L,et al.Overview of KnowledgeGraph Question Answering Enhanced by Large Language Mo-dels[J].Journal of Frontiers of Computer Science and Technology,2024,18(11):2887-2900.
[5]REN H,GALKIN M,ZHU Z,et al.Neural Graph Reasoning:A Survey on Complex Logical Query Answering[J].Transactions on Machine Learning Research,2024,2024(11):1-63.
[6]WU X,JIANG T,ZHU Y,et al.Knowledge graph for China’s genealogy[C]//2020 IEEE International Conference on Know-ledge Graph(ICKG).IEEE,2020:529-535.
[7]LUKASIEWICZ T,MARTINEZ M V,PIERIS A,et al.Fromclassical to consistent query answering under existential rules[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2015.
[8]ZHENG Y,SHI C,CAO X,et al.A meta path based method for entity set expansion in knowledge graph[J].IEEE Transactions on Big Data,2018,8(3):616-629.
[9]WANG T,GUO J,WU Z,et al.IFTA:iterative filtering byusing TF-AICL algorithm for Chinese encyclopedia knowledge refinement[J].Applied Intelligence,2021,51:6265-6293.
[10]BONATTI P A,SAURO L.On the logical properties of the nonmonotonic description logic DLN[J].Artificial Intelligence,2017,248:85-111.
[11]MANDREOLI F,MARTOGLIA R,PENZOW.Journal of Computer and System Sciences Special Issue on Query Answering on Graph-Structured Data[J].Journal of Computer and System Sciences,2016,82:1-2.
[12]EL MASSARI H,MHAMMEDI S,GHERABI N,et al.Virtual OBDA mechanism Ontop for answering SPARQL queries over Couchbase[C]//International Conference on Advanced Technologies for Humanity.Cham:Springer,2021:193-205.
[13]LYUTIKOVA L A,SHMATOVA E V.Algorithm for con-structing logical operations to identify patterns in data[C]//Biologically Inspired Cognitive Architectures Meeting.Cham:Springer,2020:212-217.
[14]LIU S,TAN N,GE Y,et al.Research on automatic question answering of generative knowledge graph based on pointer network[J].Information,2021,12(3):136.
[15]HAMILTON W L,BAJAJ P,ZITNIKM,et al.Embedding logical queries on knowledge graphs[C]//Proceedings of the 32nd International Conference on Neural Information Processing Systems.2018:2030-2041.
[16]REN H,HU W,LESKOVEC J.Query2box:Reasoning overknowledge graphs in vector space using box embeddings[C]//International Conference on Learning Representations(ICLR).Washington DC:ICLR,2020:1-13.
[17]LIU L,DU B,JIH,et al.Neural-answering logical queries on knowledge graphs[C]//Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining.2021:1087-1097.
[18]SARDINA J,SARDINA C,KELLEHERJ D,et al.Analysis of attention mechanisms in box-embedding systems[C]//Irish Conference on Artificial Intelligence and Cognitive Science.Cham:Springer,2022:68-80.
[19]ADLAKHA V , SHAH P , BEDATHUR S J.Regex Queries over Incomplete Knowledge Bases[C]//Automated Knowledge Base Construction.2021.
[20]ANDRESEL M,DOMOKOS C,STEPANOVA D,et al.A Neural-symbolic Approach for Ontology-mediated Query Answering[J].arXiv.2106.14052,2021.
[21]ZHANG Z,WANG J,CHEN J,et al.Cone:Cone embeddings for multi-hop reasoning over knowledge graphs[J].Advances in Neural Information Processing Systems,2021,34:19172-19183.
[22]CHOUDHARY N,RAO N,KATARIYA S,et al.Self-super-vised hyperboloid representations from logical queries over knowledge graphs[C]//Proceedings of the Web Conference 2021.2021:1373-1384.
[23]GEBHART T,HANSEN J,SCHRATER P.Knowledge sheaves:A sheaf-theoretic framework for knowledge graph embedding[C]//International Conference on Artificial Intelligence and Statistics.PMLR,2023:9094-9116.
[24]WAN G,DU B.Gaussianpath:a bayesian multi-hop reasoningframework for knowledge graph reasoning[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2021:4393-4401.
[25]REN H,LESKOVEC J.Beta embeddings for multi-hop logical reasoning in knowledge graphs[J].Advances in Neural Information Processing Systems,2020,33:19716-19726.
[26]CHOUDHARY N,RAO N,KATARIYAS,et al.Probabilistic entity representation model for reasoning over knowledge graphs[J].Advances in Neural Information Processing Systems,2021,34:23440-23451.
[27]YANG D,QING P,LI Y,et al.GammaE:Gamma Embeddings for Logical Queries on Knowledge Graphs[C]//2022 Confe-rence on Empirical Methods in Natural Language Processing,EMNLP 2022.2022.
[28]LONG X,ZHUANG L,AODI L,et al.Neural-based mixtureprobabilistic query embedding for answering fol queries on knowledge graphs[C]//Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing.2022:3001-3013.
[29]HUANG Z,CHIANG M F,LEE W C.Line:Logical query reasoning over hierarchical knowledge graphs[C]//Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.2022:615-625.
[30]SUN H,ARNOLD A,BEDRAX W T,et al.Faithful embed-dings for knowledge base queries[J].Advances in Neural Information Processing Systems,2020,33:22505-22516.
[31]FRIEDMAN T,BROECK G.Symbolic querying of vector spaces:Probabilistic databases meets relational embeddings[C]//Conference on Uncertainty in Artificial Intelligence.PMLR,2020:1268-1277.
[32]MINERVINI P,ARAKELYAN E,DAZAD,et al.Complex query answering with neural link predictors[C]//31st International Joint Conference on Artificial Intelligence(IJCAI 2022).International Joint Conferences on Artificial Intelligence Organization,2022:5309-5313.
[33]LUUS F P,SEN P,RIEGEL R N,et al.Logic embeddings for complex query answering:U.S.Patent Application 17/488,226[P].2023-3-30.
[34]CHEN X,HU Z,SUN Y.Fuzzy logic based logical query an-swering on knowledge graphs[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2022:3939-3948.
[35]XU Z,ZHANG W,YE P,et al.Neural-symbolic entangledframework for complex query answering[J].Advances in Neural Information Processing Systems,2022,35:1806-1819.
[36]LIN X,ZHOU G,HU T,et al.Flex:Feature-logic embeddingframework for complex knowledge graph reasoning[J].arXiv:2205.11039,2022.
[37]ZHU Z,GALKIN M,ZHANGZ,et al.Neural-symbolic models for logical queries on knowledge graphs[C]//International Conference on Machine Learning.PMLR,2022:27454-27478.
[38]LIN X,XU C,ZHOU G,et al.TFLEX:temporal feature-logic embedding framework for complex reasoning over temporal knowledge graph[C]//Advances in Neural Information Processing Systems.2024.
[39]BAI Y,LYU X,LI J,et al.Answering complex logical queries on knowledge graphs via query computation tree optimization[C]//International Conference on Machine Learning.PMLR,2023:1472-1491.
[40]WANG D,CHEN Y,GRAU B C.Efficient embeddings of logical variables for query answering over incomplete knowledge graphs[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2023:4652-4659.
[41]BAI Y,LYU X,LI J,et al.Answering complex logical queries on knowledge graphs via query computation tree optimization[C]//International Conference on Machine Learning.PMLR,2023:1472-1491.
[42]TANG Z,PEI S,PENG X,et al.TAR:Neural logical reasoning across tbox and abox[J].arXiv:2205.14591,2022.
[43]LUO H,HAIHONG E,YANG Y,et al.Nqe:N-ary query embedding for complex query answering over hyper-relational knowledge graphs[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2023:4543-4551.
[44]CUI H,PENG T,XIAO F,et al.Incorporating anticipation embedding into reinforcement learning framework for multi-hop knowledge graph question answering[J].Information Sciences,2023,619:745-761.
[45]YIN H,WANG Z,SONG Y.Rethinking Complex Queries on Knowledge Graphs with Neural Link Predictors[C]//The Twelfth International Conference on Learning Representations.2024:19722-19746.
[46]LEWKOWYCZ A,ANDREASSEN A,DOHAN D,et al.Solving quantitative reasoning problems with language models[J].Advances in Neural Information Processing Systems,2022,35:3843-3857.
[47]YAO Y,DUAN J,XU K,et al.A survey on large language mo-del(llm) security and privacy:The good,the bad,and the ugly[J].High-Confidence Computing,2024,4(2):100211.
[48]BROWN T,MANN B,RYDER N,et al.Language models arefew-shot learners[J].Advances in Neural Information Proces-sing Systems,2020,33:1877-1901.
[49]LUO L,JU J,XIONG B,et al.ChatRule:Mining Logical Rules with Large Language Models for Knowledge Graph Reasoning[C]//Advances in Knowledge Discovery and Data Mining:29th Pacific-Asia Conference on Knowledge Discovery and Data Mi-ning(PAKDD 2025).2025:314-325.
[50]LIU X,ZHAO S,SU K,et al.Mask and reason:Pre-training knowledge graph transformers for complex logical queries[C]//Proceedings of the 28th ACM SIGKDD Conference on Know-ledge Discovery and Data Mining.2022:1120-1130.
[51]LUO H,HAIHONG E,TANG Z,et al.ChatKBQA:A Gene-rate-then-Retrieve Framework for Knowledge Base Question Answering with Fine-tuned Large Language Models[C]//Findings of the Association for Computational Linguistics 2024.ACL,2024:2039-2056.
[52]CHOUDHARY N,REDDY CK.Complex logical reasoning over knowledge graphs using large language models[J].arXiv:2305.01157,2023.
[53]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.
[54]ZHANG L,ZHANG J,WANG Y,et al.FC-KBQA:A Fine-to-Coarse Composition Framework for Knowledge Base Question Answering[C]//Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics.2023:1002-1017.
[55]TAFFA T A,USBECK R.Leveraging LLMs in Scholarly Know-ledge Graph Question Answering[C]//Proceedings of the Scholarly QALD Challenge at the 22nd International Semantic Web Conference(ISWC 2023).CEUR Workshop Proceedings,2023.
[56]JIANG J,ZHOU K,ZHAO W X,et al.ReasoningLM:Enabling Structural Subgraph Reasoning in Pre-trained Language Models for Question Answering over Knowledge Graph[C]//Procee-dings of the 2023 Conference on Empirical Methods in Natural Language Processing.2023:3721-3735.
[57]JIANG J,ZHOU K,YE K M,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.
[58]HU X,WU X,SHU Y,et al.Logical form generation via multi-task learning for complex question answering over knowledge bases[C]//Proceedings of the 29th International Conference on Computational Linguistics.2022:1687-1696.
[59]LIU Y,LI Z,JIN X,et al.An in-context schema understanding method for knowledge base question answering[C]//International Conference on Knowledge Science,Engineering and Ma-nagement.Singapore:Springer,2024:419-434.
[60]AGARWAL D,DAS R,KHOSLA S,et al.Bring Your OwnKG:Self-Supervised Program Synthesis for Zero-Shot KGQA[C]//Findings of the Association for Computational Linguistics:NAACL 2024.2024:896-919.
[61]LI T,MA X,ZHUANG A,et al.Few-shot In-context Learningon Knowledge Base Question Answering[C]//Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics.2023:6966-6980.
[62]XIA T,DING L,WAN G,et al.Improving complex reasoningover knowledge graph with logic-aware curriculum tuning[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2025:12881-12889.
[63]CHAKRABORTY A.Multi-hop question answering over know-ledge graphs using large language models[J].arXiv:2404.19234,2024.
[64]LIU L,WANG Z,QIU R,et al.Logic query of thoughts:Gui-ding large language models to answer complex logic queries with knowledge graphs[J].arXiv:2404.04264,2024.
[65]SUI Y,HE Y,LIU N,et al.FiDeLiS:Faithful Reasoning inLarge Language Models for Knowledge Graph Question Answering[C]//Proceedings of the ICLR 2025 Workshop on Building Trust in Language Models and Applications.2025.
[66]HU N,WU Y,QI G,et al.An empirical study of pre-trained language models in simple knowledge graph question answering[J].World Wide Web,2023,26(5):2855-2886.
[67]WISHART D S,KNOX C,GUO A C,et al.DrugBank:a comprehensive resource for in silico drug discovery and exploration[J].Nucleic Acids Research,2006,34(S1):D668-D672.
[68]GOGLIA D,VEGA D.Structure and dynamics of growing networks of Reddit threads[J].Applied Network Science,2024,9(1):48.
[69]HIMMELSTEIN D S,LIZEE A,HESSLER C,et al.Systematic integration of biomedical knowledge prioritizes drugs for repurposing[J].Elife,2017,6:e26726.
[70]BORDES A,USUNIER N,GARCIA-DURANA,et al.Translating embeddings for modeling multi-relational data[C]//Advances in Neural Information Processing Systems.2013.
[71]BOLLACKER K,EVANS C,PARITOSH P,et al.Freebase:a collaboratively created graph database for structuring human knowledge[C]//Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data.2008:1247-1250.
[72]TOUTANOVA K,CHEN D,PANTEL P,et al.Representingtext for joint embedding of text and knowledge bases[C]//Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing.2015:1499-1509.
[73]CARLSON A,BETTERIDGE J,KISIEL B,et al.Toward an architecture for never-ending language learning[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2010:1306-1313.
[74]XIONG W,HOANG T,WANGW Y.DeepPath:A Reinforcement Learning Method for Knowledge Graph Reasoning[C]//Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing.2017:564-573.
[75]DETTMERS T,MINERVINI P,STENETORPP,et al.Convolutional 2D knowledge graph embeddings[C]//32nd AAAI Conference on Artificial Intelligence.AAAI,2018:1811-1818.
[76]MILLER G A.WordNet:a lexical database for English[J].Communications of the ACM,1995,38(11):39-41.
[77]BIZER C,LEHMANN J,KOBILAROV G,et al.Dbpedia-acrystallization point for the web of data[J].Journal of Web Semantics,2009,7(3):154-165.
[78]IOANNIDIS V N,SONG X,MANCHANDA S,et al.DRKG-Drug Repurposing Knowledge Graph for Covid-19;2020[J].arXiv:2010.09600,2020.
[79]LIN Z,WANG Q.E-commerce product networks,word-of-mouth convergence,and product sales[J].Journal of the Association for Information Systems,2018,19(1):2.
[80]GUO Y,PAN Z,HEFLIN J.LUBM:A benchmark for OWL knowledge base systems[J].Journal of Web Semantics,2005,3(2/3):158-182.
[81]ADLAKHA V,SHAH P,BEDATHUR S.Regex Queries over Incomplete Knowledge Bases[J].arXiv:2005.00480,2020.
[82]SU Y,SUN H,SADLER B,et al.On generating char-acteristic-rich question sets for qa evalua-tion[C]//Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing.2016:562-572.
[83]PELLISSIER T T,WEIKUM G,SUCHANEK F.Yago 4:Areason-able knowledge base[C]//The Semantic Web:17th International Conference(ESWC 2020).Springer,2020:583-596.
[84]YIH S W,CHANG M W,HE X,et al.Semantic pars-ing via staged query graph generation:Question an-swering with knowledge base[C]//Proceedings of the Joint Conference of the 53rd Annual Meeting of the ACL and the 7th International Joint Conference on Natural Language Processing of the AFNLP.2015.
[85]ZHOU M,HUANG M,ZHU X.An Interpretable ReasoningNetwork for Multi-Relation Question Answering[C]//Procee-dings of the 27th International Conference on Computational Linguistics.2018:2010-2022.
[86]ZHANG Y,DAI H,KOZAREVA Z,et al.Variational reasoning for question answering with knowledge graph[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2018:6069-6076..
[87]GALKIN M,TRIVEDI P,MAHESHWARI G,et al.MessagePassing for Hyper-Relational Knowledge Graphs[C]//Procee-dings of the 2020 Conference on Empirical Methods in Natural Language Processing(EMNLP).2020:7346-7359.
[88]ALIVANISTOS D,BERRENDORF M,COCHEZ M,et al.Query Embedding on Hyper-Relational Knowledge Graphs[C]//International Conference on Learning Representations.2022:6321-6342.
[89]WARD M D,BEGER A,CUTLER J,et al.Comparing GDELT and ICEWS event data[J].Analysis,2013,21(1):267-297.
[90]LEETARU K,SCHRODT P A.Gdelt:Global data on events,location,and tone,1979-2012[C]//ISA Annual Convention.Citeseer,2013:1-49.
[91]SHAO P,ZHANG D,YANG G,et al.Tucker decompo-sition-based temporal knowledge graph completion[J].Knowledge-Based Systems,2022,238:107841.
[92]LAN Y,JIANG J.Query graph generation for answering multi-hop complex questions from knowledge bases[C]//Annual Meeting of the Association for Computational Linguistics.2020.
[93]BORDES A,USUNIER N,CHOPRA S,et al.Large-scale simple question answering with memory networks[J].arXiv:1506.02075,2015.
[94]JIANG K,WU D,JIANG H.FreebaseQA:A new fac-toid QA data set matching trivia-style question-answer pairs with Freebase[C]//Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies.2019:318-323.
[95]MIN S,MICHAEL J,HAJISHIRZI H,et al.AmbigQA:An-swering Ambiguous Open-domain Questions[C]//Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing(EMNLP).2020:5783-5797.
[96]VOORHEES E M,BUCKLAND L.Overview of the TREC2003 Question Answering Track[C]//TREC.2003:54-68.
[97]CAI Z,VASCONCELOS N.Cascade r-cnn:Delving into highquality object detection[C]//Proceedings of the IEEE Confe-rence on Computer Vision and Pattern Recognition.2018:6154-6162.
[98]LAN Y,HE S,LIU K,et al.Knowledge reasoning via jointlymodeling knowledge graphs and soft rules[J].Applied Sciences,2023,13(19):10660.
[1] GUO Luxiang, WANG Yueyu, LI Qianyue, LI Shasha, LIU Xiaodong, JI Bin, YU Jie. Comprehensive Survey of LLM-based Agent Operating Systems [J]. Computer Science, 2026, 53(1): 1-11.
[2] LIU Lilong, LIU Guoming, QI Baoyuan, DENG Xueshan, XUE Dizhan, QIAN Shengsheng. Efficient Inference Techniques of Large Models in Real-world Applications:A Comprehensive Survey [J]. Computer Science, 2026, 53(1): 12-28.
[3] SHAO Xinyi, ZHU Jingwei, ZHANG Liang. LLM-based Business Process Adaptation Method to Respond Long-tailed Changes [J]. Computer Science, 2026, 53(1): 29-38.
[4] LIU Leyuan, CHEN Gege, WU Wei, WANG Yong, ZHOU Fan. Survey of Data Classification and Grading Studies [J]. Computer Science, 2025, 52(9): 195-211.
[5] CAI Qihang, XU Bin, DONG Xiaodi. Knowledge Graph Completion Model Using Semantically Enhanced Prompts and Structural Information [J]. Computer Science, 2025, 52(9): 282-293.
[6] ZHONG Boyang, RUAN Tong, ZHANG Weiyan, LIU Jingping. Collaboration of Large and Small Language Models with Iterative Reflection Framework for Clinical Note Summarization [J]. Computer Science, 2025, 52(9): 294-302.
[7] WANG Limei, HAN Linrui, DU Zuwei, ZHENG Ri, SHI Jianzhong, LIU Yiqun. Privacy Policy Compliance Detection Method for Mobile Application Based on Large LanguageModel [J]. Computer Science, 2025, 52(8): 1-16.
[8] WANG Dongsheng. Multi-defendant Legal Judgment Prediction with Multi-turn LLM and Criminal Knowledge Graph [J]. Computer Science, 2025, 52(8): 308-316.
[9] CHI Yiyan, QI Mingze, HUANGPENG Qizi, DUAN Xiaojun. Degree Distribution Inference Method for Complex Networks Based on Controllable PreferentialSampling [J]. Computer Science, 2025, 52(7): 82-91.
[10] LI Maolin, LIN Jiajie, YANG Zhenguo. Confidence-guided Prompt Learning for Multimodal Aspect-level Sentiment Analysis [J]. Computer Science, 2025, 52(7): 241-247.
[11] CHEN Jinyin, XI Changkun, ZHENG Haibin, GAO Ming, ZHANG Tianxin. Survey of Security Research on Multimodal Large Language Models [J]. Computer Science, 2025, 52(7): 315-341.
[12] TU Ji, XIAO Wendong, TU Wenji, LI Lijian. Application of Large Language Models in Medical Education:Current Situation,Challenges and Future [J]. Computer Science, 2025, 52(6A): 240400121-6.
[13] LI Bo, MO Xian. Application of Large Language Models in Recommendation System [J]. Computer Science, 2025, 52(6A): 240400097-7.
[14] ZOU Rui, YANG Jian, ZHANG Kai. Low-resource Vietnamese Speech Synthesis Based on Phoneme Large Language Model andDiffusion Model [J]. Computer Science, 2025, 52(6A): 240700138-6.
[15] ZHOU Lei, SHI Huaifeng, YANG Kai, WANG Rui, LIU Chaofan. Intelligent Prediction of Network Traffic Based on Large Language Model [J]. Computer Science, 2025, 52(6A): 241100058-7.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!