{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T15:27:01Z","timestamp":1759332421001,"version":"3.44.0"},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,12,16]],"date-time":"2024-12-16T00:00:00Z","timestamp":1734307200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,16]],"date-time":"2024-12-16T00:00:00Z","timestamp":1734307200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Mass Spectrometry Key Technology R&D and Clinical Application of Anhui Province Jointly Constructed Discipline Key Experiments","award":["2023ZPLH07"],"award-info":[{"award-number":["2023ZPLH07"]}]},{"name":"Humanity and Social Science Research Project of Anhui Provincial Education Department","award":["2022AH050224"],"award-info":[{"award-number":["2022AH050224"]}]},{"name":"Housing urban and rural construction science and technology plan project of Anhui Province","award":["2022-YF082"],"award-info":[{"award-number":["2022-YF082"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Data Sci Anal"],"published-print":{"date-parts":[[2025,10]]},"DOI":"10.1007\/s41060-024-00699-3","type":"journal-article","created":{"date-parts":[[2024,12,16]],"date-time":"2024-12-16T11:09:37Z","timestamp":1734347377000},"page":"3869-3888","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A few-shot learning method based on knowledge graph in large language models"],"prefix":"10.1007","volume":"20","author":[{"given":"FeiLong","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Donghui","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jose","family":"Aguilar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinyi","family":"Cui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,12,16]]},"reference":[{"key":"699_CR1","doi-asserted-by":"publisher","first-page":"100426","DOI":"10.1016\/j.dajour.2024.100426","volume":"10","author":"A Dash","year":"2024","unstructured":"Dash, A., Darshana, S., Yadav, D.K., et al.: A clinical named entity recognition model using pretrained word embedding and deep neural networks. Decis. Anal. J. 10, 100426 (2024)","journal-title":"Decis. Anal. J."},{"key":"699_CR2","doi-asserted-by":"publisher","first-page":"103435","DOI":"10.1016\/j.jbi.2020.103435","volume":"106","author":"Y Li","year":"2020","unstructured":"Li, Y., Du, G., Xiang, Y., et al.: Towards Chinese clinical named entity recognition by dynamic embedding using domain-specific knowledge. J. Biomed. Inf. 106, 103435 (2020)","journal-title":"J. Biomed. Inf."},{"issue":"3","key":"699_CR3","doi-asserted-by":"publisher","first-page":"611","DOI":"10.18280\/ria.370310","volume":"37","author":"R Bani","year":"2023","unstructured":"Bani, R., Amri, S., Zenkouar, L., et al.: Deep neural networks for part-of-speech tagging in under-resourced Amazigh. Revue d\u2019Intelligence Artificielle 37(3), 611 (2023)","journal-title":"Revue d\u2019Intelligence Artificielle"},{"issue":"7","key":"699_CR4","doi-asserted-by":"publisher","first-page":"e17175","DOI":"10.1016\/j.heliyon.2023.e17175","volume":"9","author":"H Sintayehu","year":"2023","unstructured":"Sintayehu, H., Lehal, G.S.: Improving part-of-speech tagging in Amharic language using deep neural network. Heliyon 9(7), e17175\u2013e17175 (2023)","journal-title":"Heliyon"},{"key":"699_CR5","doi-asserted-by":"publisher","first-page":"123374","DOI":"10.1016\/j.eswa.2024.123374","volume":"248","author":"Z Feng","year":"2024","unstructured":"Feng, Z., Mao, K., Zhou, H.: Adaptive micro-and macro-knowledge incorporation for hierarchical text classification. Expert Syst. Appl. 248, 123374 (2024)","journal-title":"Expert Syst. Appl."},{"issue":"1","key":"699_CR6","doi-asserted-by":"publisher","first-page":"2087","DOI":"10.1038\/s41598-023-29013-0","volume":"13","author":"Y Guo","year":"2023","unstructured":"Guo, Y., Yan, J., Xu, D., et al.: Feature-enhanced text-inception model for Chinese long text classification. Sci. Rep. 13(1), 2087\u20132087 (2023)","journal-title":"Sci. Rep."},{"key":"699_CR7","first-page":"120","volume":"2024","author":"G Luca","year":"2024","unstructured":"Luca, G., George, P., Giovanni, S., et al.: GSM: a generalized approach to supervised meta-blocking for scalable entity resolution. Inf. Syst. 2024, 120 (2024)","journal-title":"Inf. Syst."},{"key":"699_CR8","doi-asserted-by":"crossref","unstructured":"Wang, Y., Kordi, Y., Mishra, S., et al.: Self-instruct: aligning language model with self generated instructions. arXiv:2212.10560 (2022)","DOI":"10.18653\/v1\/2023.acl-long.754"},{"key":"699_CR9","doi-asserted-by":"crossref","unstructured":"Maynez, J., Narayan, S., Bohnet, B., et al.: On faithfulness and factuality in abstractive summarization. arXiv:2005.00661 (2020)","DOI":"10.18653\/v1\/2020.acl-main.173"},{"issue":"3\u20134","key":"699_CR10","doi-asserted-by":"publisher","first-page":"300","DOI":"10.1016\/0370-2693(95)00051-L","volume":"347","author":"GW Anderson","year":"1995","unstructured":"Anderson, G.W., Diego, J.: Measures of fine tuning. Phys. Lett. B Castano 347(3\u20134), 300\u2013308 (1995)","journal-title":"Phys. Lett. B Castano"},{"key":"699_CR11","unstructured":"Toneva, M., Sordoni, A., Tachet des Combes, R., et al.: An empirical study of example forgetting during deep neural network learning. arXiv:1812.05159 (2018)"},{"issue":"3","key":"699_CR12","doi-asserted-by":"publisher","first-page":"109148","DOI":"10.1016\/j.isci.2024.109148","volume":"27","author":"H Luo","year":"2024","unstructured":"Luo, H., Yin, W., Wang, J., et al.: Drug-drug interactions prediction based on deep learning and knowledge graph: a review. iScience 27(3), 109148 (2024)","journal-title":"iScience"},{"key":"699_CR13","doi-asserted-by":"publisher","first-page":"120268","DOI":"10.1016\/j.ins.2024.120268","volume":"662","author":"F Zhang","year":"2024","unstructured":"Zhang, F., Li, X.: Knowledge-enhanced online doctor recommendation framework based on knowledge graph and joint learning. Inf. Sci. 662, 120268 (2024)","journal-title":"Inf. Sci."},{"key":"699_CR14","doi-asserted-by":"publisher","first-page":"111468","DOI":"10.1016\/j.knosys.2024.111468","volume":"289","author":"H Zhong","year":"2024","unstructured":"Zhong, H., Li, W., Zhang, Q., et al.: A unified embedding-based relation completion framework for knowledge graph. Knowl. Based Syst. 289, 111468 (2024)","journal-title":"Knowl. Based Syst."},{"issue":"2","key":"699_CR15","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1006\/knac.1993.1008","volume":"5","author":"TR Gruber","year":"1993","unstructured":"Gruber, T.R.: A translation approach to portable ontology specifications. Knowl. Acquis.. Acquis. 5(2), 199\u2013220 (1993)","journal-title":"Knowl. Acquis.. Acquis."},{"issue":"1\u20134","key":"699_CR16","first-page":"2","volume":"48","author":"E Lisa","year":"2016","unstructured":"Lisa, E., W\u00f6\u00df, W.: Towards a definition of knowledge graphs. SEMANTiCS Posters Demos SuCCESS 48(1\u20134), 2 (2016)","journal-title":"SEMANTiCS Posters Demos SuCCESS"},{"key":"699_CR17","doi-asserted-by":"publisher","first-page":"102282","DOI":"10.1016\/j.artmed.2022.102282","volume":"127","author":"An Ying","year":"2022","unstructured":"Ying, An., Xia, X., Chen, X., et al.: Chinese clinical named entity recognition via multi-head self-attention based BiLSTM-CRF. Artif. Intell. Med.. Intell. Med. 127, 102282\u2013102282 (2022)","journal-title":"Artif. Intell. Med.. Intell. Med."},{"key":"699_CR18","doi-asserted-by":"crossref","unstructured":"Qin, P., Xu, W., Guo, J.: Designing an adaptive attention mechanism for relation classification. In: International Joint Conference on Neural Networks, pp. 4356\u20134362. IEEE (2017)","DOI":"10.1109\/IJCNN.2017.7966407"},{"key":"699_CR19","doi-asserted-by":"crossref","unstructured":"Pan, S., Luo, L., Wang, Y., et al.: Unifying large language models and knowledge graphs: a roadmap. arXiv:2306.08302 (2024)","DOI":"10.1109\/TKDE.2024.3352100"},{"issue":"3","key":"699_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3386252","volume":"53","author":"Y Wang","year":"2020","unstructured":"Wang, Y., Yao, Q., Kwok, J., et al.: Generalizing from a few examples: a survey on few-shot learning. ACM Comput. Surv. (csur) 53(3), 1\u201334 (2020)","journal-title":"ACM Comput. Surv. (csur)"},{"issue":"3","key":"699_CR21","doi-asserted-by":"publisher","first-page":"1809","DOI":"10.3390\/app13031809","volume":"13","author":"T Wang","year":"2023","unstructured":"Wang, T., Chen, C., Dong, X., et al.: A novel method of production line bearing fault diagnosis based on 2D image and cross-domain few-shot learning. Appl. Sci. 13(3), 1809\u20131809 (2023)","journal-title":"Appl. Sci."},{"key":"699_CR22","first-page":"30","volume":"2017","author":"S Jake","year":"2017","unstructured":"Jake, S., Swersky, K., Zemel, R.: Prototypical networks for few-shot learning. Adv. Neural. Inf. Process. Syst. 2017, 30 (2017)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"699_CR23","unstructured":"Zhang, X., Qiang, Y., Sung, F., et al.: Deep comparison: relation columns for few-shot learning. arXiv:1811.07100 (2018)"},{"key":"699_CR24","doi-asserted-by":"crossref","unstructured":"Min, S., Lyu, X., Holtzman, A., et al.: Rethinking the role of demonstrations: what makes in-context learning work?. arXiv:2202.12837 (2022)","DOI":"10.18653\/v1\/2022.emnlp-main.759"},{"key":"699_CR25","unstructured":"Devlin, J., Chang, M.W., Lee, K., et al.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018)"},{"key":"699_CR26","unstructured":"Radford, A., Narasimhan, K., Salimans, T., et al.: Improving language understanding by generative pre-training. (2018)"},{"issue":"140","key":"699_CR27","first-page":"1","volume":"21","author":"C Raffel","year":"2020","unstructured":"Raffel, C., Shazeer, N., Roberts, A., et al.: Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res. 21(140), 1\u201367 (2020)","journal-title":"J. Mach. Learn. Res."},{"key":"699_CR28","unstructured":"Tom, B., Mann, B., Ryder, N., et al.: Language models are few-shot learners. 33, 1877\u20131901 (2020)"},{"key":"699_CR29","doi-asserted-by":"crossref","unstructured":"Zhou, C., Li, Q., Li, C., et al.: A comprehensive survey on pretrained foundation models: a history from BERT to ChatGPT. arXiv:2302.09419 (2023)","DOI":"10.1007\/s13042-024-02443-6"},{"key":"699_CR30","first-page":"30","volume":"2017","author":"A Vaswani","year":"2017","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., et al.: Attention is all you need. Adv. Neural. Inf. Process. Syst. 2017, 30 (2017)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"3","key":"699_CR31","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1016\/S0167-6393(98)00018-1","volume":"24","author":"G Potamianos","year":"1998","unstructured":"Potamianos, G., Jelinek, F.: A study of n-gram and decision tree letter language modeling methods. Speech Commun.Commun. 24(3), 171\u2013192 (1998)","journal-title":"Speech Commun.Commun."},{"issue":"8","key":"699_CR32","first-page":"9","volume":"1","author":"R Alec","year":"2019","unstructured":"Alec, R., Wu, J., Rewon, C., et al.: Language models are unsupervised multitask learners. OpenAI Blog 1(8), 9 (2019)","journal-title":"OpenAI Blog"},{"key":"699_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.frl.2022.103617","volume":"53","author":"M Leippold","year":"2023","unstructured":"Leippold, M.: Thus spoke GPT-3: interviewing a large-language model on climate finance. Financ. Res. Lett.. Res. Lett. 53, 103617 (2023)","journal-title":"Financ. Res. Lett.. Res. Lett."},{"issue":"240","key":"699_CR34","first-page":"1","volume":"24","author":"A Chowdhery","year":"2023","unstructured":"Chowdhery, A., Narang, S., Devlin, J., et al.: Palm: scaling language modeling with pathways. J. Mach. Learn. Res. 24(240), 1\u2013113 (2023)","journal-title":"J. Mach. Learn. Res."},{"key":"699_CR35","doi-asserted-by":"crossref","unstructured":"Liu, X., Ji, K., Fu, Y., et al.: P-Tuning v2: prompt tuning can be comparable to fine-tuning universally across scales and tasks. arXiv:2110.07602 (2021)","DOI":"10.18653\/v1\/2022.acl-short.8"},{"key":"699_CR36","doi-asserted-by":"crossref","unstructured":"Li, X.L., Liang, P.: Prefix-tuning: optimizing continuous prompts for generation. arXiv:2101.00190 (2021)","DOI":"10.18653\/v1\/2021.acl-long.353"},{"key":"699_CR37","doi-asserted-by":"crossref","unstructured":"Lester, B., Al-Rfou, R., Constant, N.: The power of scale for parameter-efficient prompt tuning. arXiv:2104.08691 (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.243"},{"key":"699_CR38","unstructured":"Chen, M., Tworek, J., Jun, H., et al.: Evaluating large language models trained on code. arXiv:2107.03374 (2021)"},{"key":"699_CR39","unstructured":"Neelakantan, A., Xu, T., Puri, R., et al.: Text and code embeddings by contrastive pre-training. arXiv:2201.10005 (2022)"},{"key":"699_CR40","unstructured":"Nathan, L., Louis, C., von Leandeo, W., et al.: Illustrating reinforcement learning from human feedback (rlhf). Hugging Face Blog. (2022)"},{"key":"699_CR41","unstructured":"Wei, J., Wang, X., Schuurmans, D. et al.: Chain-of-thought prompting elicits reasoning in large language models. In: 36th Conference on Neural Information Processing Systems, NeurIPS 2022, November 28, 2022\u2013December 9, 2022, New Orleans, LA, United states, 2022b. Vol. 35. Neural Information Processing Systems Foundation. (2022)"},{"key":"699_CR42","unstructured":"Zhang, Z., Zhang, A., Li, M., et al.: Automatic chain of thought prompting in large language models. arXiv:2210.03493 (2022)"},{"key":"699_CR43","unstructured":"Wang, X., Wei, J., Schuurmans, D., et al.: Self-consistency improves chain of thought reasoning in language models. arXiv:2203.11171 (2022)"},{"key":"699_CR44","first-page":"547","volume":"2023","author":"AJ Urquhart","year":"2023","unstructured":"Urquhart, A.J., Ren, J., Dusenberry, M.W., et al.: A simple zero-shot prompt weighting technique to improve prompt ensembling in text-image models. Int. Conf. Mach. Learn. PMLR 2023, 547\u2013568 (2023)","journal-title":"Int. Conf. Mach. Learn. PMLR"},{"key":"699_CR45","unstructured":"Wei, J., Tay, Y., Bommasani, R., et al.: Emergent abilities of large language models. arXiv:2206.07682 (2022)"},{"key":"699_CR46","unstructured":"Wu, C., Lin, W., Zhang, X., et al.: Pmc-llama: further finetuning llama on medical papers. arXiv:2304.14454 (2023)"},{"key":"699_CR47","doi-asserted-by":"publisher","first-page":"110141","DOI":"10.1016\/j.dib.2024.110141","volume":"53","author":"X Yang","year":"2024","unstructured":"Yang, X., Li, C., He, R., et al.: CAISHI: a benchmark histopathological H&E image dataset for cervical adenocarcinoma in situ identification, retrieval and few-shot learning evaluation. Data Brief 53, 110141 (2024)","journal-title":"Data Brief"},{"issue":"4","key":"699_CR48","doi-asserted-by":"publisher","first-page":"e26559","DOI":"10.1016\/j.heliyon.2024.e26559","volume":"10","author":"J Lin","year":"2024","unstructured":"Lin, J., Zhu, S., Yin, M., et al.: Few-shot learning for the classification of intestinal tuberculosis and Crohn\u2019s disease on endoscopic images: a novel learn-to-learn framework. Heliyon 10(4), e26559 (2024)","journal-title":"Heliyon"},{"key":"699_CR49","doi-asserted-by":"publisher","first-page":"106026","DOI":"10.1016\/j.istruc.2024.106026","volume":"61","author":"J Luo","year":"2024","unstructured":"Luo, J., Zheng, F., Sun, S.: A few-shot learning method for vibration-based damage detection in civil structures. Structures 61, 106026 (2024)","journal-title":"Structures"},{"key":"699_CR50","doi-asserted-by":"crossref","unstructured":"Gao, Y., Li, R., John, C., et al.: Leveraging a medical knowledge graph into large language models for diagnosis prediction. arXiv:2308.14321 (2023)","DOI":"10.2196\/preprints.58670"},{"key":"699_CR51","unstructured":"Jiang, X., Zhang, R., Xu, Y., et al.: HyKGE: a hypothesis knowledge graph enhanced framework for accurate and reliable medical LLMs responses. arXiv:2312.15883 (2023)"},{"key":"699_CR52","doi-asserted-by":"crossref","unstructured":"Wen, Y., Wang, Z., Sun, J.: Mindmap: knowledge graph prompting sparks graph of thoughts in large language models. arXiv:2308.09729 (2023)","DOI":"10.18653\/v1\/2024.acl-long.558"},{"key":"699_CR53","first-page":"10436","volume":"36","author":"BR Andrus","year":"2022","unstructured":"Andrus, B.R., Yeganeh, N., Shilong, C., et al.: Enhanced story comprehension for large language models through dynamic document-based knowledge graphs. Proc. AAAI Conf. Artif. Intell. 36, 10436\u201310444 (2022)","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"699_CR54","unstructured":"Du, Z., Qian, Y., Liu, X., et al.: All nlp tasks are generation tasks: a general pretraining framework. arXiv:2103.10360 (2021)"},{"key":"699_CR55","doi-asserted-by":"crossref","unstructured":"Papineni, K., Roukos, S., Ward, T., et al.: BLEU: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311\u2013318 (2002)","DOI":"10.3115\/1073083.1073135"},{"key":"699_CR56","first-page":"74","volume":"2004","author":"CY Lin","year":"2004","unstructured":"Lin, C.Y.: Rouge: a package for automatic evaluation of summaries. Text Summar. Branches Out 2004, 74\u201381 (2004)","journal-title":"Text Summar. Branches Out"},{"key":"699_CR57","unstructured":"Zeng, A., Liu, X., Du, Z., et al.: Glm-130b: an open bilingual pre-trained model. In: The Eleventh International Conference on Learning Representations (2022)"},{"key":"699_CR58","unstructured":"Hu, E.J., Shen, Y., Wallis, P., et al.: Lora: low-rank adaptation of large language models. arXiv:2106.09685 (2021)"},{"key":"699_CR59","unstructured":"Banerjee, S., Lavie, A.: METEOR: an automatic metric for MT evaluation with improved correlation with human judgments. In: Proceedings of the ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and\/or Summarization, pp. 65\u201372 (2005)"}],"container-title":["International Journal of Data Science and Analytics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41060-024-00699-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s41060-024-00699-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41060-024-00699-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T10:53:24Z","timestamp":1758797604000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s41060-024-00699-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,16]]},"references-count":59,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,10]]}},"alternative-id":["699"],"URL":"https:\/\/doi.org\/10.1007\/s41060-024-00699-3","relation":{},"ISSN":["2364-415X","2364-4168"],"issn-type":[{"type":"print","value":"2364-415X"},{"type":"electronic","value":"2364-4168"}],"subject":[],"published":{"date-parts":[[2024,12,16]]},"assertion":[{"value":"22 August 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 December 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 December 2024","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 conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}