{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T10:27:06Z","timestamp":1772101626497,"version":"3.50.1"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T00:00:00Z","timestamp":1771977600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T00:00:00Z","timestamp":1772064000000},"content-version":"vor","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cloud Comp"],"DOI":"10.1186\/s13677-026-00855-z","type":"journal-article","created":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T01:37:48Z","timestamp":1771983468000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Advancing faithful KBQA services via prompt-driven knowledge graph-enhanced LLMs in cloud"],"prefix":"10.1186","volume":"15","author":[{"given":"Zishun","family":"Rui","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shengjie","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shucun","family":"Fu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenzheng","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shengjun","family":"Xue","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,2,25]]},"reference":[{"key":"855_CR1","doi-asserted-by":"crossref","unstructured":"Xu X, Wu F, Bilal M, Xia X, Dou W, Yao L, Zhong W (2024) Xrl-shap-cache: an explainable reinforcement learning approach for intelligent edge service caching in content delivery networks. Sci China Inf Sci 67(7):170303","DOI":"10.1007\/s11432-023-3987-y"},{"key":"855_CR2","doi-asserted-by":"crossref","unstructured":"Banerjee S (2024) Intelligent cloud systems: ai-driven enhancements in scalability and predictive resource management. Int J Adv Res Sci, Commun Technol 266\u2013276","DOI":"10.48175\/IJARSCT-22840"},{"key":"855_CR3","doi-asserted-by":"publisher","unstructured":"Fu S, Dong F, Chen R, Shen D, Zhang J, He Q (2025) Multi-dimensional training optimization for efficient federated synergy learning. IEEE Trans Mob Comput 24(7):6243\u20136258. https:\/\/doi.org\/10.1109\/TMC.2025.3540566","DOI":"10.1109\/TMC.2025.3540566"},{"key":"855_CR4","doi-asserted-by":"crossref","unstructured":"Talia D (2012) Clouds meet agents: toward intelligent cloud services. IEEE Internet Comput 16(2):78\u201381","DOI":"10.1109\/MIC.2012.28"},{"key":"855_CR5","doi-asserted-by":"crossref","unstructured":"Cui M, Zeng C, Xu X, Bilal M, Xia X (2024) Mw-unet: multi-scale weighted connection unet for identification and classification of non-meteorological clutter over big radar data. Big Data Min Analytics 8(1):65\u201377","DOI":"10.26599\/BDMA.2024.9020032"},{"key":"855_CR6","doi-asserted-by":"publisher","unstructured":"Xu X, Meng K, Xiang H, Cui G, Xia X, Dou W (2025) Blockchain-enabled secure, fair, and scalable data sharing in zero-trust edge-end environment. IEEE J Sel Areas Commun 43(6):2056\u20132069. https:\/\/doi.org\/10.1109\/JSAC.2025.3560007","DOI":"10.1109\/JSAC.2025.3560007"},{"key":"855_CR7","unstructured":"Xiang H, Wang G, Xiao Y, Di F, Gao R, Zhang Y, Han X (2025) Reliable and secure anomaly detection in heterogeneous federated learning: a comprehensive review. Big Data Min Analytics"},{"issue":"5","key":"855_CR8","doi-asserted-by":"publisher","first-page":"7063","DOI":"10.1007\/s40747-024-01527-8","volume":"10","author":"T Guo","year":"2024","unstructured":"Guo T, Yang Q, Wang C, Liu Y, Li P, Tang J, Li D, Wen Y (2024) Knowledgenavigator: leveraging large language models for enhanced reasoning over knowledge graph. Complex Intell Syst 10(5):7063\u20137076","journal-title":"Complex Intell Syst"},{"key":"855_CR9","doi-asserted-by":"crossref","unstructured":"Xu D, Li X, Zhang Z, Lin Z, Zhu Z, Zheng Z, Wu X, Zhao X, Xu T, Chen E (2025) Harnessing large language models for knowledge graph question answering via adaptive multi-aspect retrieval-augmentation. In Proceedings of the AAAI Conference on Artificial Intelligence, vol 39. pp 25570\u201325578","DOI":"10.1609\/aaai.v39i24.34747"},{"key":"855_CR10","doi-asserted-by":"publisher","DOI":"10.26599\/TST.2024.9010253","volume-title":"Modeling adaptive traffic signal controller with communicable graph multi-agent action reference","author":"S Zhu","year":"2025","unstructured":"Zhu S, Lyu H, Peng K, Hu H, Liu G, Xu X (2025) Modeling adaptive traffic signal controller with communicable graph multi-agent action reference. Tsinghua Science and Technology"},{"key":"855_CR11","doi-asserted-by":"crossref","unstructured":"Wang L, Xu W, Lan Y, Hu Z, Lan Y, Lee RK-W, Lim E-P (2023). Plan-and-solve prompting: improving zero-shot chain-of-thought reasoning by large language models. arXiv preprint arXiv:2305.04091","DOI":"10.18653\/v1\/2023.acl-long.147"},{"key":"855_CR12","doi-asserted-by":"crossref","unstructured":"Huang J, Chang K-C-C (2022). Towards reasoning in large language models: a survey. arXiv preprint arXiv:2212.10403","DOI":"10.18653\/v1\/2023.findings-acl.67"},{"issue":"5","key":"855_CR13","doi-asserted-by":"publisher","first-page":"2227","DOI":"10.26599\/TST.2024.9010105","volume":"30","author":"J Xu","year":"2025","unstructured":"Xu J, Xiang H, Zang S, Bilal M, Khan M, Cui G (2025) A dqn-based edge offloading method for smart city pollution control. Tsinghua Sci Technol 30(5):2227\u20132242","journal-title":"Tsinghua Sci Technol"},{"key":"855_CR14","doi-asserted-by":"crossref","unstructured":"Hong R, Zhang H, Zhao H, Yu D, Zhang C (2023). Faithful question answering with monte-carlo planning. arXiv preprint arXiv:2305.02556","DOI":"10.18653\/v1\/2023.acl-long.218"},{"issue":"1","key":"855_CR15","doi-asserted-by":"publisher","first-page":"18","DOI":"10.26599\/BDMA.2024.9020063","volume":"8","author":"W Guo","year":"2024","unstructured":"Guo W, Du S, Hu J, Teng F, Yang Y, Li T (2024) Rp-kgc: a knowledge graph completion model integrating rule-based knowledge for pretraining and inference. Big Data Min Analytics 8(1):18\u201330","journal-title":"Big Data Min Analytics"},{"key":"855_CR16","doi-asserted-by":"crossref","unstructured":"Yih W-T, Richardson M, Meek C, Chang M-W, Suh J (2016) The value of semantic parse labeling for knowledge base question answering. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp 201\u2013206","DOI":"10.18653\/v1\/P16-2033"},{"key":"855_CR17","doi-asserted-by":"crossref","unstructured":"Talmor A, Berant J (2018) The web as a knowledge-base for answering complex questions. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol 1 (Long Papers), pp. 641\u2013651","DOI":"10.18653\/v1\/N18-1059"},{"key":"855_CR18","doi-asserted-by":"crossref","unstructured":"Wang K, Duan F, Wang S, Li P, Xian Y, Yin C, Rong W, Xiong Z (2023). Knowledge-driven cot: exploring faithful reasoning in llms for knowledge-intensive question answering. arXiv preprint arXiv:2308.13259","DOI":"10.18293\/SEKE2023-023"},{"issue":"2","key":"855_CR19","doi-asserted-by":"publisher","first-page":"782","DOI":"10.26599\/TST.2024.9010006","volume":"30","author":"F Lu","year":"2024","unstructured":"Lu F, Qi Q, Qin H (2024) Joint extraction of uygur medicine knowledge with edge computing. Tsinghua Sci Technol 30(2):782\u2013795","journal-title":"Tsinghua Sci Technol"},{"key":"855_CR20","doi-asserted-by":"crossref","unstructured":"Sun H, Bedrax-Weiss T, Cohen W (2019) Pullnet: open domain question answering with iterative retrieval on knowledge bases and text. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, pp 2380\u20132390","DOI":"10.18653\/v1\/D19-1242"},{"issue":"3","key":"855_CR21","doi-asserted-by":"publisher","first-page":"661","DOI":"10.26599\/BDMA.2024.9020093","volume":"8","author":"Q Wang","year":"2025","unstructured":"Wang Q, Yu C, Chen S, Fang W, Xiong N (2025) Joint adaptive resolution selection and conditional early exiting for efficient video recognition on edge devices. Big Data Min Analytics 8(3):661\u2013677","journal-title":"Big Data Min Analytics"},{"key":"855_CR22","unstructured":"Wang X, Wei J, Schuurmans D, Le Q, Chi E, Narang S, Chowdhery A, Zhou D (2022). Self-consistency improves chain of thought reasoning in language models. arXiv preprint arXiv:2203.11171"},{"key":"855_CR23","doi-asserted-by":"crossref","unstructured":"Haveliwala TH (2002) Topic-sensitive pagerank. In Proceedings of the 11th International Conference on World Wide Web, pp 517\u2013526","DOI":"10.1145\/511446.511513"},{"key":"855_CR24","doi-asserted-by":"crossref","unstructured":"He G, Lan Y, Jiang J, Zhao WX, Wen J-R (2021) Improving multi-hop knowledge base question answering by learning intermediate supervision signals. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining, pp 553\u2013561","DOI":"10.1145\/3437963.3441753"},{"key":"855_CR25","doi-asserted-by":"crossref","unstructured":"Yasunaga M, Ren H, Bosselut A, Liang P, Leskovec J (2021). Qa-gnn: reasoning with language models and knowledge graphs for question answering. arXiv preprint arXiv:2104.06378","DOI":"10.18653\/v1\/2021.naacl-main.45"},{"key":"855_CR26","doi-asserted-by":"crossref","unstructured":"Sun H, Dhingra B, Zaheer M, Mazaitis K, Salakhutdinov R, Cohen WW (2018). Open domain question answering using early fusion of knowledge bases and text. arXiv preprint arXiv:1809.00782","DOI":"10.18653\/v1\/D18-1455"},{"key":"855_CR27","doi-asserted-by":"crossref","unstructured":"Dong G, Li R, Wang S, Zhang Y, Xian Y, Xu W (2023) Bridging the kb-text gap: leveraging structured knowledge-aware pre-training for kbqa. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, pp 3854\u20133859","DOI":"10.1145\/3583780.3615150"},{"key":"855_CR28","doi-asserted-by":"publisher","first-page":"102204","DOI":"10.1016\/j.datak.2023.102204","volume":"147","author":"E Yang","year":"2023","unstructured":"Yang E, Hao F, Shang J, Chen X, Park D-S (2023) Bt-ckbqa: an efficient approach for Chinese knowledge base question answering. Data Knowl Eng 147:102204","journal-title":"Data Knowl Eng"},{"key":"855_CR29","doi-asserted-by":"crossref","unstructured":"Ding W, Li J, Luo L, Qu Y (2024) Enhancing complex question answering over knowledge graphs through evidence pattern retrieval. In Proceedings of the ACM Web Conference, pp 2106\u20132115","DOI":"10.1145\/3589334.3645563"},{"key":"855_CR30","first-page":"11809","volume":"36","author":"S Yao","year":"2023","unstructured":"Yao S, Yu D, Zhao J, Shafran I, Griffiths T, Cao Y, Narasimhan K (2023) Tree of thoughts: deliberate problem solving with large language models. Adv Neural Inf Process Syst 36:11809\u201311822","journal-title":"Adv Neural Inf Process Syst"},{"key":"855_CR31","doi-asserted-by":"crossref","unstructured":"Besta M, Blach N, Kubicek A, Gerstenberger R, Podstawski M, Gianinazzi L, Gajda J, Lehmann T, Niewiadomski H, Nyczyk P et al. (2024) Graph of thoughts: solving elaborate problems with large language models. In Proceedings of the AAAI Conference on Artificial Intelligence, vol 38. pp 17682\u201317690","DOI":"10.1609\/aaai.v38i16.29720"},{"key":"855_CR32","doi-asserted-by":"crossref","unstructured":"Li Z, Fan S, Gu Y, Li X, Duan Z, Dong B, Liu N, Wang J (2024) Flexkbqa: a flexible llm-powered framework for few-shot knowledge base question answering. In Proceedings of the AAAI Conference on Artificial Intelligence, vol 38. pp 18608\u201318616","DOI":"10.1609\/aaai.v38i17.29823"},{"key":"855_CR33","unstructured":"Jiang J, Zhou K, Zhao WX, Wen J-R (2022). Unikgqa: unified retrieval and reasoning for solving multi-hop question answering over knowledge graph. arXiv preprint arXiv:2212.00959"},{"key":"855_CR34","unstructured":"Zhang Z, Wen L, Zhao W (2025) Rule-kbqa: rule-guided reasoning for complex knowledge base question answering with large language models. In Proceedings of the 31st International Conference on Computational Linguistics, pp 8399\u20138417"},{"key":"855_CR35","doi-asserted-by":"crossref","unstructured":"Long X, Zhuang L, Li A, Yao M, Wang S (2025) Eperm: an evidence path enhanced reasoning model for knowledge graph question and answering. In Proceedings of the AAAI Conference on Artificial Intelligence, vol 39. pp 12282\u201312290","DOI":"10.1609\/aaai.v39i12.33338"},{"key":"855_CR36","first-page":"1877","volume":"33","author":"T Brown","year":"2020","unstructured":"Brown T, Mann B, Ryder N, Subbiah M, Kaplan JD, Dhariwal P, Neelakantan A, Shyam P, Sastry G, Askell A et al. (2020) Language models are few-shot learners. Adv Neural Inf Process Syst 33:1877\u20131901","journal-title":"Adv Neural Inf Process Syst"},{"key":"855_CR37","first-page":"24824","volume":"35","author":"J Wei","year":"2022","unstructured":"Wei J, Wang X, Schuurmans D, Bosma M, Xia F, Chi E, Le QV, Zhou D et al. (2022) Chain-of-thought prompting elicits reasoning in large language models. Adv Neural Inf Process Syst 35:24824\u201324837","journal-title":"Adv Neural Inf Process Syst"},{"key":"855_CR38","doi-asserted-by":"crossref","unstructured":"Gao J, Cao J, Bu R, Zhu N, Guan W, Yu H (2025) Promoting knowledge base question answering by directing llms to generate task-relevant logical forms. In Proceedings of the AAAI Conference on Artificial Intelligence, vol 39. pp 23914\u201323922","DOI":"10.1609\/aaai.v39i22.34564"},{"key":"855_CR39","doi-asserted-by":"crossref","unstructured":"Wang S, Yu Y. (2025) iQuest: an iterative question-guided framework for knowledge base question answering. arXiv preprint arXiv:2506.01784","DOI":"10.18653\/v1\/2025.acl-long.760"},{"key":"855_CR40","unstructured":"Huang J, She Q, Jiang W, Wu H, Hao Y, Xu T, Wu F (2024) Qdmr-based planning-and-solving prompting for complex reasoning tasks. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pp 13395\u201313406"},{"key":"855_CR41","unstructured":"Touvron H, Martin L, Stone K, Albert P, Almahairi A, Babaei Y, Bashlykov N, Batra S, Bhargava P, Bhosale S, et al. (2023) Llama 2: open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288"},{"key":"855_CR42","doi-asserted-by":"crossref","unstructured":"Zhang J, Zhang X, Yu J, Tang J, Tang J, Li C, Chen H (2022) Subgraph retrieval enhanced model for multi-hop knowledge base question answering. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp 5773\u20135784","DOI":"10.18653\/v1\/2022.acl-long.396"},{"key":"855_CR43","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.91","volume-title":"Query graph generation for answering multi-hop complex questions from knowledge bases","author":"Y Lan","year":"2020","unstructured":"Lan Y, Jiang J (2020) Query graph generation for answering multi-hop complex questions from knowledge bases. Association for Computational Linguistics"},{"key":"855_CR44","unstructured":"Gu Y, Su Y (2022). Arcaneqa: dynamic program induction and contextualized encoding for knowledge base question answering. arXiv preprint arXiv:2204.08109"},{"key":"855_CR45","doi-asserted-by":"crossref","unstructured":"Ye X, Yavuz S, Hashimoto K, Zhou Y, Xiong C (2021). Rng-kbqa: generation augmented iterative ranking for knowledge base question answering. arXiv preprint arXiv:2109.08678","DOI":"10.18653\/v1\/2022.acl-long.417"},{"key":"855_CR46","unstructured":"Chung HW, Hou L, Longpre S, Zoph B, Tay Y, Fedus W, Li Y, Wang X, Dehghani M, Brahma S et al. (2024) Scaling instruction-finetuned language models. J Mach Learn Res 25(70):1\u201353"},{"key":"855_CR47","unstructured":"Taori R, Gulrajani I, Zhang T, Dubois Y, Li X, Guestrin C, Liang P, Hashimoto TB (2023) Stanford alpaca: an instruction-following llama model"},{"key":"855_CR48","unstructured":"Yu D, Zhang S, Ng P, Zhu H, Li AH, Wang J, Hu Y, Wang W, Wang Z, Xiang B (2022). Decaf: joint decoding of answers and logical forms for question answering over knowledge bases. arXiv preprint arXiv:2210.00063"},{"key":"855_CR49","unstructured":"Luo L, Li Y, Haffari G, Pan S (2024) Reasoning on graphs: faithful and interpretable large language model reasoning. In ICLR 2024: The Twelfth International Conference on Learning Representations"},{"key":"855_CR50","doi-asserted-by":"crossref","unstructured":"Cai G, Tian R, Yang L, Jia Y, Li L, Wang J (2026) Efficient inference for edge large language models: a survey. Tsinghua Sci Technol 31(3):1365\u20131380","DOI":"10.26599\/TST.2025.9010166"},{"key":"855_CR51","doi-asserted-by":"crossref","unstructured":"Fu S, Dong F, Shen D, Zhang J, Huang Z, He Q (2023) Joint optimization of device selection and resource allocation for multiple federations in federated edge learning. IEEE Trans Serv Comput 17(1):251\u2013262","DOI":"10.1109\/TSC.2023.3342435"}],"container-title":["Journal of Cloud Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13677-026-00855-z","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13677-026-00855-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13677-026-00855-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T09:36:35Z","timestamp":1772098595000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s13677-026-00855-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,25]]},"references-count":51,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["855"],"URL":"https:\/\/doi.org\/10.1186\/s13677-026-00855-z","relation":{},"ISSN":["2192-113X"],"issn-type":[{"value":"2192-113X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,25]]},"assertion":[{"value":"8 December 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 January 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 February 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"26"}}