{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T22:25:52Z","timestamp":1770330352300,"version":"3.49.0"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T00:00:00Z","timestamp":1764201600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T00:00:00Z","timestamp":1767052800000},"content-version":"vor","delay-in-days":33,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J. King Saud Univ. Comput. Inf. Sci."],"published-print":{"date-parts":[[2026,1]]},"DOI":"10.1007\/s44443-025-00380-0","type":"journal-article","created":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T12:47:41Z","timestamp":1764247661000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Semantically Guided Multi-Stage Graph Learning for Inductive Multi-Label Text Classification"],"prefix":"10.1007","volume":"38","author":[{"given":"Mingqiang","family":"Wu","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,11,27]]},"reference":[{"key":"380_CR1","unstructured":"Arora S, Liang Y, Ma T (2017) A simple but tough-to-beat baseline for sentence embeddings. In: 5th International Conference on Learning Representations"},{"issue":"1","key":"380_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2024.103952","volume":"62","author":"Q Cheng","year":"2025","unstructured":"Cheng Q, Shi W (2025) Hierarchical multi-label text classification of tourism resources using a label-aware dual graph attention network. Information Processing & Management 62(1):103952. https:\/\/doi.org\/10.1016\/j.ipm.2024.103952","journal-title":"Information Processing & Management"},{"key":"380_CR3","doi-asserted-by":"publisher","unstructured":"Cho K, van Merrienboer B, G\u00fcl\u00e7ehre \u00c7, et\u00a0al (2014) Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 1724\u20131734, https:\/\/doi.org\/10.3115\/v1\/d14-1179","DOI":"10.3115\/v1\/d14-1179"},{"key":"380_CR4","doi-asserted-by":"publisher","unstructured":"Devlin J, Chang M, Lee K, et\u00a0al (2019) Bert: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp 4171\u20134186, https:\/\/doi.org\/10.18653\/v1\/n19-1423","DOI":"10.18653\/v1\/n19-1423"},{"key":"380_CR5","doi-asserted-by":"publisher","unstructured":"Dey R, Salem FM (2017) Gate-variants of gated recurrent unit (gru) neural networks. In: 2017 IEEE 60th international midwest symposium on circuits and systems (MWSCAS), IEEE, pp 1597\u20131600, https:\/\/doi.org\/10.1109\/MWSCAS.2017.8053243","DOI":"10.1109\/MWSCAS.2017.8053243"},{"key":"380_CR6","doi-asserted-by":"publisher","unstructured":"Gao L, Wang L (2025) Cql-gnn: Coupled question-label graph neural networks for multi-label educational question classification. Journal of King Saud University - Computer and Information Sciences 37(7). https:\/\/doi.org\/10.1007\/s44443-025-00208-x","DOI":"10.1007\/s44443-025-00208-x"},{"key":"380_CR7","unstructured":"Hamilton W, Ying Z, Leskovec J (2017) Inductive representation learning on large graphs. Advances in neural information processing systems 30"},{"key":"380_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.105873","volume":"91","author":"Y He","year":"2024","unstructured":"He Y, Xiong Q, Ke C et al (2024) Mcict: Graph convolutional network-based end-to-end model for multi-label classification of imbalanced clinical text. Biomed Signal Process Control 91:105873","journal-title":"Biomed Signal Process Control"},{"key":"380_CR9","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S (1997) Long short-term memory. Neural Computation MIT-Press. https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","journal-title":"Neural Computation MIT-Press"},{"key":"380_CR10","doi-asserted-by":"publisher","unstructured":"Hou S, Qian Y, Chen J, et\u00a0al (2025a) Hiee: Hierarchical feature fusion for chinese event extraction. J King Saud Univ Comput Inf Sci 37(4). https:\/\/doi.org\/10.1007\/s44443-025-00079-2","DOI":"10.1007\/s44443-025-00079-2"},{"key":"380_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2024.128667","volume":"611","author":"S Hou","year":"2025","unstructured":"Hou S, Qian Y, Chen J et al (2025) Hiner: Hierarchical feature fusion for chinese named entity recognition. Neurocomputing 611:128667. https:\/\/doi.org\/10.1016\/j.neucom.2024.128667","journal-title":"Neurocomputing"},{"key":"380_CR12","doi-asserted-by":"publisher","first-page":"2704","DOI":"10.1145\/3366423.3380027","volume":"2020","author":"Z Hu","year":"2020","unstructured":"Hu Z, Dong Y, Wang K et al (2020) Heterogeneous graph transformer. Proceedings of the web conference 2020:2704\u20132710. https:\/\/doi.org\/10.1145\/3366423.3380027","journal-title":"Proceedings of the web conference"},{"key":"380_CR13","unstructured":"Huang YH, Chen YH, Chen YS (2022) Contexting: granting document-wise contextual embeddings to graph neural networks for inductive text classification. In: Proceedings of the 29th international conference on computational linguistics, pp 1163\u20131168"},{"key":"380_CR14","doi-asserted-by":"publisher","unstructured":"Kiechle J, Lang DM, Fischer SM, et\u00a0al (2024) Graph neural networks: A suitable alternative to mlps in latent 3d medical image classification? arXiv preprint arXiv:2407.17219https:\/\/doi.org\/10.48550\/arXiv.2407.17219","DOI":"10.48550\/arXiv.2407.17219"},{"key":"380_CR15","unstructured":"Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. In: International Conference on Learning Representations"},{"key":"380_CR16","doi-asserted-by":"publisher","unstructured":"Kowsari K, Brown DE, Heidarysafa M, et\u00a0al (2017) Hdltex: Hierarchical deep learning for text classification. In: 2017 16th IEEE international conference on machine learning and applications (ICMLA), IEEE, pp 364\u2013371, https:\/\/doi.org\/10.1109\/ICMLA.2017.0-134","DOI":"10.1109\/ICMLA.2017.0-134"},{"key":"380_CR17","unstructured":"Kumar A, Irsoy O, Ondruska P, et\u00a0al (2016) Ask me anything: dynamic memory networks for natural language processing. In: Proceedings of the 33rd International Conference on International Conference on Machine Learning-Volume 48, pp 1378\u20131387"},{"key":"380_CR18","doi-asserted-by":"crossref","unstructured":"Li I, Feng A, Wu H, et\u00a0al (2022a) Ligcn: Label-interpretable graph convolutional networks for multi-label text classification. In: Proceedings of the 2nd Workshop on Deep Learning on Graphs for Natural Language Processing (DLG4NLP 2022), pp 60\u201370","DOI":"10.18653\/v1\/2022.dlg4nlp-1.7"},{"issue":"2","key":"380_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3495162","volume":"13","author":"Q Li","year":"2022","unstructured":"Li Q, Peng H, Li J et al (2022) A survey on text classification: From traditional to deep learning. ACM Transactions on Intelligent Systems and Technology (TIST) 13(2):1\u201341. https:\/\/doi.org\/10.1145\/3495162","journal-title":"ACM Transactions on Intelligent Systems and Technology (TIST)"},{"issue":"19","key":"380_CR20","doi-asserted-by":"publisher","first-page":"9363","DOI":"10.1007\/s10489-024-05666-w","volume":"54","author":"X Li","year":"2024","unstructured":"Li X, You B, Peng Q et al (2024) Dual-view graph convolutional network for multi-label text classification. Appl Intell 54(19):9363\u20139380. https:\/\/doi.org\/10.1007\/s10489-024-05666-w","journal-title":"Appl Intell"},{"key":"380_CR21","unstructured":"Li Y, Zemel R, Brockschmidt M, et\u00a0al (2016) Gated graph sequence neural networks. In: Proceedings of ICLR\u201916"},{"issue":"3","key":"380_CR22","doi-asserted-by":"publisher","first-page":"1171","DOI":"10.1007\/s10618-023-00992-y","volume":"38","author":"Z Liang","year":"2024","unstructured":"Liang Z, Guo J, Qiu W et al (2024) When graph convolution meets double attention: online privacy disclosure detection with multi-label text classification. Data Min Knowl Disc 38(3):1171\u20131192. https:\/\/doi.org\/10.1007\/s10618-023-00992-y","journal-title":"Data Min Knowl Disc"},{"key":"380_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2024.111797","volume":"295","author":"M Lin","year":"2024","unstructured":"Lin M, Wang T, Zhu Y et al (2024) A heterogeneous directed graph attention network for inductive text classification using multilevel semantic embeddings. Knowl-Based Syst 295:111797. https:\/\/doi.org\/10.1016\/j.knosys.2024.111797","journal-title":"Knowl-Based Syst"},{"key":"380_CR24","doi-asserted-by":"publisher","unstructured":"Lin N, Qin G, Wang G, et\u00a0al (2023) An effective deployment of contrastive learning in multi-label text classification. In: Findings of the Association for Computational Linguistics: ACL 2023, pp 8730\u20138744, https:\/\/doi.org\/10.18653\/v1\/2023.findings-acl.556","DOI":"10.18653\/v1\/2023.findings-acl.556"},{"issue":"2","key":"380_CR25","doi-asserted-by":"publisher","first-page":"1028","DOI":"10.1109\/TCYB.2019.2932439","volume":"51","author":"J Ma","year":"2021","unstructured":"Ma J, Zhang H, Chow TW (2021) Multilabel classification with label-specific features and classifiers: A coarse-and fine-tuned framework. IEEE transactions on cybernetics 51(2):1028\u20131042. https:\/\/doi.org\/10.1109\/TCYB.2019.2932439","journal-title":"IEEE transactions on cybernetics"},{"key":"380_CR26","doi-asserted-by":"publisher","unstructured":"Mohammad S, Bravo-Marquez F, Salameh M, et\u00a0al (2018) Semeval-2018 task 1: Affect in tweets. In: Proceedings of the 12th international workshop on semantic evaluation, pp 1\u201317. https:\/\/doi.org\/10.18653\/v1\/s18-1001","DOI":"10.18653\/v1\/s18-1001"},{"key":"380_CR27","doi-asserted-by":"publisher","unstructured":"Pal A, Selvakumar M, Sankarasubbu M (2020) Magnet: Multi-label text classification using attention-based graph neural network. In: Proceedings of the 12th International Conference on Agents and Artificial Intelligence, ICAART 2020, Volume 2, Valletta, Malta, February 22-24, 2020. SCITEPRESS, pp 494\u2013505. https:\/\/doi.org\/10.5220\/0008940304940505","DOI":"10.5220\/0008940304940505"},{"key":"380_CR28","doi-asserted-by":"publisher","unstructured":"Piao Y, Lee S, Lee D, et\u00a0al (2022) Sparse structure learning via graph neural networks for inductive document classification. In: Proceedings of the AAAI conference on artificial intelligence, pp 11165\u201311173. https:\/\/doi.org\/10.1609\/aaai.v36i10.21366","DOI":"10.1609\/aaai.v36i10.21366"},{"issue":"4","key":"380_CR29","doi-asserted-by":"publisher","first-page":"2013","DOI":"10.1007\/s10994-023-06369-y","volume":"113","author":"M Proietti","year":"2024","unstructured":"Proietti M, Ragno A, Rosa BL et al (2024) Explainable ai in drug discovery: self-interpretable graph neural network for molecular property prediction using concept whitening. Mach Learn 113(4):2013\u20132044. https:\/\/doi.org\/10.1007\/s10994-023-06369-y","journal-title":"Mach Learn"},{"key":"380_CR30","doi-asserted-by":"publisher","unstructured":"Pu T, Yin S, Li W, et\u00a0al (2021) Graph convolutional network exploring label relations for multi-label text classification. In: Pacific Rim International Conference on Artificial Intelligence, pp 127\u2013139. https:\/\/doi.org\/10.1007\/978-3-030-89363-7_10","DOI":"10.1007\/978-3-030-89363-7_10"},{"key":"380_CR31","doi-asserted-by":"publisher","unstructured":"Ragesh R, Sellamanickam S, Iyer A, et\u00a0al (2021) Hetegcn: heterogeneous graph convolutional networks for text classification. In: Proceedings of the 14th ACM international conference on web search and data mining, pp 860\u2013868. https:\/\/doi.org\/10.1145\/3437963.3441746","DOI":"10.1145\/3437963.3441746"},{"key":"380_CR32","doi-asserted-by":"publisher","unstructured":"Shi J, Wu X, Liu X, et\u00a0al (2022) Inductive light graph convolution network for text classification based on word-label graph. In: International Conference on Intelligent Information Processing, Springer, pp 42\u201355. https:\/\/doi.org\/10.1007\/978-3-031-03948-5_4","DOI":"10.1007\/978-3-031-03948-5_4"},{"key":"380_CR33","doi-asserted-by":"publisher","unstructured":"Torba F, Gravier C, Laclau C, et\u00a0al (2025) Decoding the hierarchy: A hybrid approach to hierarchical multi-label text classification. In: Advances in Information Retrieval - 47th European Conference on Information Retrieval (ECIR), Lecture Notes in Computer Science, vol\u00a01. Springer, Cham, pp 405\u2013420. https:\/\/doi.org\/10.1007\/978-3-031-88708-6_26","DOI":"10.1007\/978-3-031-88708-6_26"},{"key":"380_CR34","doi-asserted-by":"publisher","unstructured":"U SCL, He J, Guti\u00e9rrez-Basulto V, et\u00a0al (2023) Instances and labels: Hierarchy-aware joint supervised contrastive learning for hierarchical multi-label text classification. In: Findings of the Association for Computational Linguistics: EMNLP 2023, pp 8858\u20138875. https:\/\/doi.org\/10.18653\/v1\/2023.findings-emnlp.594, https:\/\/aclanthology.org\/2023.findings-emnlp.594","DOI":"10.18653\/v1\/2023.findings-emnlp.594"},{"key":"380_CR35","unstructured":"Vaswani A, Shazeer N, Parmar N, et\u00a0al (2017) Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp 6000\u20136010"},{"key":"380_CR36","unstructured":"Veli\u010dkovi\u0107 P, Cucurull G, Casanova A, et\u00a0al (2018) Graph attention networks. In: International Conference on Learning Representations"},{"key":"380_CR37","doi-asserted-by":"publisher","unstructured":"Vu HT, Nguyen MT, Nguyen VC, et\u00a0al (2022) Label correlation based graph convolutional network for multi-label text classification. In: 2022 International Joint Conference on Neural Networks (IJCNN), IEEE, pp 01\u201308. https:\/\/doi.org\/10.1109\/IJCNN55064.2022.9892542","DOI":"10.1109\/IJCNN55064.2022.9892542"},{"issue":"12","key":"380_CR38","doi-asserted-by":"publisher","first-page":"14759","DOI":"10.1007\/s10489-022-04106-x","volume":"53","author":"HT Vu","year":"2023","unstructured":"Vu HT, Nguyen MT, Nguyen VC et al (2023) Label-representative graph convolutional network for multi-label text classification. Appl Intell 53(12):14759\u201314774. https:\/\/doi.org\/10.1007\/s10489-022-04106-x","journal-title":"Appl Intell"},{"key":"380_CR39","doi-asserted-by":"publisher","unstructured":"Wang B, Liu J, Chen S, et\u00a0al (2021) A residual dynamic graph convolutional network for multi-label text classification. In: CCF International Conference on Natural Language Processing and Chinese Computing, pp 664\u2013675. https:\/\/doi.org\/10.1007\/978-3-030-88480-2_53","DOI":"10.1007\/978-3-030-88480-2_53"},{"key":"380_CR40","doi-asserted-by":"publisher","unstructured":"Wang K, Han SC, Poon J (2022a) Induct-gcn: Inductive graph convolutional networks for text classification. In: 2022 26th International Conference on Pattern Recognition (ICPR), IEEE, pp 1243\u20131249. https:\/\/doi.org\/10.1109\/ICPR56361.2022.9956075","DOI":"10.1109\/ICPR56361.2022.9956075"},{"key":"380_CR41","doi-asserted-by":"publisher","unstructured":"Wang R, Dai X, et\u00a0al (2022b) Contrastive learning-enhanced nearest neighbor mechanism for multi-label text classification. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Association for Computational Linguistics, pp 672\u2013679. https:\/\/doi.org\/10.18653\/V1\/2022.ACL-SHORT.75","DOI":"10.18653\/V1\/2022.ACL-SHORT.75"},{"key":"380_CR42","unstructured":"Wang S, Zhou G, Lu J, et\u00a0al (2025) Pre-trained semantic interaction based inductive graph neural networks for text classification. In: Proceedings of the 2025 Conference on Computational Linguistics (COLING), pp 812\u2013827. https:\/\/aclanthology.org\/2025.coling-main.54\/"},{"key":"380_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2025.103639","volume":"126","author":"Y Wang","year":"2026","unstructured":"Wang Y, Wang Q, Yu H et al (2026) Gcfa: Generative class feature fusion with agent attention for medical text classification. Information Fusion 126:103639. https:\/\/doi.org\/10.1016\/j.inffus.2025.103639","journal-title":"Information Fusion"},{"key":"380_CR44","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2025.113664","volume":"183","author":"M Wu","year":"2025","unstructured":"Wu M (2025) Multi-source graph contrastive learning with dual-level dynamic fusion of structure and feature for inductive semi-supervised short text classification. Appl Soft Comput 183:113664. https:\/\/doi.org\/10.1016\/j.asoc.2025.113664","journal-title":"Appl Soft Comput"},{"key":"380_CR45","doi-asserted-by":"publisher","unstructured":"Xie Q, Huang J, Du P, et\u00a0al (2021) Inductive topic variational graph auto-encoder for text classification. In: proceedings of the 2021 conference of the North American chapter of the association for computational linguistics: human language technologies, pp 4218\u20134227. https:\/\/doi.org\/10.18653\/v1\/2021.naacl-main.333","DOI":"10.18653\/v1\/2021.naacl-main.333"},{"key":"380_CR46","doi-asserted-by":"publisher","DOI":"10.1109\/TETCI.2023.3301774","author":"X Yan","year":"2024","unstructured":"Yan X, Huang H, Jin Y et al (2024) Neural architecture search via multi-hashing embedding and graph tensor networks for multilingual text classification. IEEE Transactions on Emerging Topics in Computational Intelligence. https:\/\/doi.org\/10.1109\/TETCI.2023.3301774","journal-title":"IEEE Transactions on Emerging Topics in Computational Intelligence"},{"key":"380_CR47","unstructured":"Yang P, Sun X, Li W, et\u00a0al (2018) Sgm: Sequence generation model for multi-label classification. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3915\u20133926"},{"issue":"3","key":"380_CR48","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3450352","volume":"39","author":"T Yang","year":"2021","unstructured":"Yang T, Hu L, Shi C et al (2021) Hgat: Heterogeneous graph attention networks for semi-supervised short text classification. ACM Transactions on Information Systems (TOIS) 39(3):1\u201329. https:\/\/doi.org\/10.1145\/3450352","journal-title":"ACM Transactions on Information Systems (TOIS)"},{"key":"380_CR49","doi-asserted-by":"publisher","unstructured":"Ye C, Zhang L, He Y, et\u00a0al (2021) Beyond text: Incorporating metadata and label structure for multi-label document classification using heterogeneous graphs. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp 3162\u20133171. https:\/\/doi.org\/10.18653\/v1\/2021.emnlp-main.253","DOI":"10.18653\/v1\/2021.emnlp-main.253"},{"key":"380_CR50","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.111303","volume":"284","author":"D Zeng","year":"2024","unstructured":"Zeng D, Zha E, Kuang J et al (2024) Multi-label text classification based on semantic-sensitive graph convolutional network. Knowl-Based Syst 284:111303. https:\/\/doi.org\/10.1016\/j.knosys.2023.111303","journal-title":"Knowl-Based Syst"},{"key":"380_CR51","doi-asserted-by":"crossref","unstructured":"Zhang Y, Yu X, Cui Z, et\u00a0al (2020) Every document owns its structure: Inductive text classification via graph neural networks. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp 334\u2013339","DOI":"10.18653\/v1\/2020.acl-main.31"},{"key":"380_CR52","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2022.108403","volume":"171","author":"X Zhao","year":"2022","unstructured":"Zhao X, Zhang X, Emmanuel A (2022) Research and demonstration of technology opportunity identification model based on text classification and core patents. Computers & Industrial Engineering 171:108403. https:\/\/doi.org\/10.1016\/j.cie.2022.108403","journal-title":"Computers & Industrial Engineering"},{"key":"380_CR53","doi-asserted-by":"publisher","unstructured":"Zheng K, Wang Y, Yao Q, et\u00a0al (2022) Simplified graph learning for inductive short text classification. In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pp 10717\u201310724. https:\/\/doi.org\/10.18653\/v1\/2022.emnlp-main.735","DOI":"10.18653\/v1\/2022.emnlp-main.735"},{"key":"380_CR54","doi-asserted-by":"publisher","unstructured":"Zhu Y, Xu Y, Yu F, et\u00a0al (2021) Graph contrastive learning with adaptive augmentation. In: Proceedings of the Web Conference, pp 2069\u20132080. https:\/\/doi.org\/10.1145\/3442381.3449802","DOI":"10.1145\/3442381.3449802"},{"issue":"7","key":"380_CR55","doi-asserted-by":"publisher","first-page":"6698","DOI":"10.1109\/TKDE.2022.3193657","volume":"35","author":"D Zong","year":"2023","unstructured":"Zong D, Sun S (2023) Bgnn-xml: Bilateral graph neural networks for extreme multi-label text classification. IEEE Trans Knowl Data Eng 35(7):6698\u20136709. https:\/\/doi.org\/10.1109\/TKDE.2022.3193657","journal-title":"IEEE Trans Knowl Data Eng"}],"container-title":["Journal of King Saud University Computer and Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44443-025-00380-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44443-025-00380-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44443-025-00380-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T09:50:57Z","timestamp":1770285057000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44443-025-00380-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,27]]},"references-count":55,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["380"],"URL":"https:\/\/doi.org\/10.1007\/s44443-025-00380-0","relation":{},"ISSN":["1319-1578","2213-1248"],"issn-type":[{"value":"1319-1578","type":"print"},{"value":"2213-1248","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,27]]},"assertion":[{"value":"4 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 November 2025","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 that they have no known competing financial interests that could have appeared to influence the work in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of Interest"}}],"article-number":"5"}}