{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T09:53:55Z","timestamp":1781603635895,"version":"3.54.5"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T00:00:00Z","timestamp":1781568000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T00:00:00Z","timestamp":1781568000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["No. 72071145"],"award-info":[{"award-number":["No. 72071145"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Knowl Inf Syst"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1007\/s10115-026-02794-2","type":"journal-article","created":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T09:18:49Z","timestamp":1781601529000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Klcse: Kullback\u2013Leibler divergence contrastive sentence embedding"],"prefix":"10.1007","volume":"68","author":[{"given":"Zhongguo","family":"Xu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Xiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,16]]},"reference":[{"key":"2794_CR1","doi-asserted-by":"publisher","unstructured":"Liu Y, Cheng H, Klopfer R, Gormley MR, Schaaf T (2021) Effective convolutional attention network for multi-label clinical document classification. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 5941\u20135953. https:\/\/doi.org\/10.18653\/v1\/2021.emnlp-main.481. https:\/\/aclanthology.org\/2021.emnlp-main.481","DOI":"10.18653\/v1\/2021.emnlp-main.481"},{"key":"2794_CR2","doi-asserted-by":"publisher","unstructured":"Liu D, Gong Y, Fu J, Yan Y, Chen J, Jiang D, Lv J, Duan N (2020) RikiNet: reading wikipedia pages for natural question answering. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL), pp. 6762\u20136771. https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.604. https:\/\/aclanthology.org\/2020.acl-main.604","DOI":"10.18653\/v1\/2020.acl-main.604"},{"key":"2794_CR3","doi-asserted-by":"publisher","unstructured":"Wu B, Zhang Z, Wang J, Zhao H (2022) Sentence-aware contrastive learning for open-domain passage retrieval. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL), pp. 1062\u20131074. https:\/\/doi.org\/10.18653\/v1\/2022.acl-long.76. https:\/\/aclanthology.org\/2022.acl-long.76","DOI":"10.18653\/v1\/2022.acl-long.76"},{"key":"2794_CR4","doi-asserted-by":"publisher","unstructured":"Ma X, Zhang Z, Zhao H (2022) Structural characterization for dialogue disentanglement. In: Muresan, S., Nakov, P., Villavicencio, A. (eds.) Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 285\u2013297. Association for Computational Linguistics, Dublin, Ireland. https:\/\/doi.org\/10.18653\/v1\/2022.acl-long.23. https:\/\/aclanthology.org\/2022.acl-long.23","DOI":"10.18653\/v1\/2022.acl-long.23"},{"key":"2794_CR5","doi-asserted-by":"publisher","unstructured":"Devlin J, Chang M-W, Lee K, Toutanova K (2019) BERT: pre-training of deep bidirectional transformers for language understanding, pp. 4171\u20134186. https:\/\/doi.org\/10.18653\/v1\/N19-1423. https:\/\/www.aclweb.org\/anthology\/N19-1423","DOI":"10.18653\/v1\/N19-1423"},{"key":"2794_CR6","unstructured":"Zhuang L, Wayne L, Ya S, Jun Z (2021) A robustly optimized BERT pre-training approach with post-training. In: Li S, Sun M, Liu Y, Wu H, Liu K, Che W, He S, Rao G (eds.) Proceedings of the 20th Chinese National Conference on Computational Linguistics, pp. 1218\u20131227. Chinese Information Processing Society of China, Huhhot, China. https:\/\/aclanthology.org\/2021.ccl-1.108\/"},{"key":"2794_CR7","doi-asserted-by":"publisher","unstructured":"Reimers N, Gurevych I (2019) Sentence-BERT: sentence embeddings using Siamese BERT-networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 3982\u20133992. Association for Computational Linguistics, Hong Kong, China. https:\/\/doi.org\/10.18653\/v1\/D19-1410. https:\/\/aclanthology.org\/D19-1410","DOI":"10.18653\/v1\/D19-1410"},{"key":"2794_CR8","unstructured":"Wu Z, Wang S, Gu J, Khabsa M, Sun F, Ma H (2020) Clear: contrastive learning for sentence representation.(arXiv preprint)"},{"key":"2794_CR9","doi-asserted-by":"publisher","unstructured":"Gao T, Yao X, Chen D (2021) SimCSE: simple contrastive learning of sentence embeddings. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 6894\u20136910. Association for Computational Linguistics, Online and Punta Cana, Dominican Republic . https:\/\/doi.org\/10.18653\/v1\/2021.emnlp-main.552. https:\/\/aclanthology.org\/2021.emnlp-main.552","DOI":"10.18653\/v1\/2021.emnlp-main.552"},{"key":"2794_CR10","unstructured":"Wu X, Gao C, Zang L, Han J, Wang Z, Hu S (2022) ESimCSE: enhanced sample building method for contrastive learning of unsupervised sentence embedding. In: Calzolari N, Huang CR, Kim H, Pustejovsky J, Wanner L, Choi KS, Ryu PM, Chen HH, Donatelli L, Ji H, Kurohashi S, Paggio P, Xue N, Kim S, Hahm Y, He Z, Lee TK, Santus E, Bond F, Na SH (eds.) Proceedings of the 29th International Conference on Computational Linguistics, pp. 3898\u20133907. International Committee on Computational Linguistics, Gyeongju, Republic of Korea. https:\/\/aclanthology.org\/2022.coling-1.342\/"},{"key":"2794_CR11","doi-asserted-by":"publisher","unstructured":"Klein T, Nabi M (2022) SCD: self-contrastive decorrelation of sentence embeddings. In: Muresan S, Nakov P, Villavicencio A (eds.) Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 394\u2013400. Association for Computational Linguistics, Dublin, Ireland. https:\/\/doi.org\/10.18653\/v1\/2022.acl-short.44. https:\/\/aclanthology.org\/2022.acl-short.44","DOI":"10.18653\/v1\/2022.acl-short.44"},{"key":"2794_CR12","doi-asserted-by":"crossref","unstructured":"Chuang Y-S, Dangovski R, Luo H, Zhang Y, Chang S, Soljacic M, Li S-W, Yih W-T, Kim Y, Glass J (2022) DiffCSE: difference-based contrastive learning for sentence embeddings. Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)","DOI":"10.18653\/v1\/2022.naacl-main.311"},{"key":"2794_CR13","doi-asserted-by":"publisher","unstructured":"Wu X, Gao C, Lin Z, Han J, Wang Z, Hu S (2022) InfoCSE: Information-aggregated contrastive learning of sentence embeddings. In: Goldberg Y, Kozareva Z, Zhang Y (eds.) Findings of the Association for Computational Linguistics: EMNLP 2022, pp. 3060\u20133070. Association for Computational Linguistics, Abu Dhabi, United Arab Emirates. https:\/\/doi.org\/10.18653\/v1\/2022.findings-emnlp.223. https:\/\/aclanthology.org\/2022.findings-emnlp.223\/","DOI":"10.18653\/v1\/2022.findings-emnlp.223"},{"key":"2794_CR14","doi-asserted-by":"publisher","unstructured":"Klein T, Nabi M (2023) miCSE: mutual information contrastive learning for low-shot sentence embeddings. In: Rogers A, Boyd-Graber J, Okazaki N (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 6159\u20136177. Association for Computational Linguistics, Toronto, Canada. https:\/\/doi.org\/10.18653\/v1\/2023.acl-long.339. https:\/\/aclanthology.org\/2023.acl-long.339","DOI":"10.18653\/v1\/2023.acl-long.339"},{"key":"2794_CR15","doi-asserted-by":"crossref","unstructured":"Yoda S, Tsukagoshi H, Sasano R, Takeda K (2024) Sentence representations via Gaussian embedding. In: Graham Y, Purver M (eds.) Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 418\u2013425. Association for Computational Linguistics, St. Julian\u2019s, Malta. https:\/\/aclanthology.org\/2024.eacl-short.36","DOI":"10.18653\/v1\/2024.eacl-short.36"},{"key":"2794_CR16","volume-title":"Probabilistic graphical models: principles and techniques-adaptive computation and machine learning","author":"D Koller","year":"2009","unstructured":"Koller D, Friedman N (2009) Probabilistic graphical models: principles and techniques-adaptive computation and machine learning. The MIT Press, Cambridge"},{"key":"2794_CR17","unstructured":"Hinton G, Vinyals O, Dean J (2015) Distilling the Knowledge in a Neural Network. https:\/\/arxiv.org\/abs\/1503.02531"},{"key":"2794_CR18","doi-asserted-by":"publisher","unstructured":"Bowman SR, Angeli G, Potts C, Manning CD (2015) A large annotated corpus for learning natural language inference, pp. 632\u2013642. https:\/\/doi.org\/10.18653\/v1\/D15-1075. https:\/\/www.aclweb.org\/anthology\/D15-1075","DOI":"10.18653\/v1\/D15-1075"},{"key":"2794_CR19","doi-asserted-by":"publisher","unstructured":"Williams A, Nangia N, Bowman S (2018) A broad-coverage challenge corpus for sentence understanding through inference, pp. 1112\u20131122 . https:\/\/doi.org\/10.18653\/v1\/N18-1101. https:\/\/www.aclweb.org\/anthology\/N18-1101","DOI":"10.18653\/v1\/N18-1101"},{"key":"2794_CR20","unstructured":"Agirre E, Cer D, Diab M, Gonzalez-Agirre A (2012) SemEval-2012 task 6: a pilot on semantic textual similarity. In: *SEM 2012: The First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the Main Conference and the Shared Task (SemEval 2012), pp. 385\u2013393 . https:\/\/www.aclweb.org\/anthology\/S12-1051"},{"key":"2794_CR21","unstructured":"Agirre E, Cer D, Diab M, Gonzalez-Agirre A, Guo W (2013) *SEM 2013 shared task: semantic textual similarity. In: Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 1: Proceedings of the Main Conference and the Shared Task: Semantic Textual Similarity, pp. 32\u201343. https:\/\/www.aclweb.org\/anthology\/S13-1004"},{"key":"2794_CR22","doi-asserted-by":"publisher","unstructured":"Agirre E, Banea C, Cardie C, Cer D, Diab M, Gonzalez-Agirre A, Guo W, Mihalcea R, Rigau G, Wiebe J (2014) SemEval-2014 task 10: multilingual semantic textual similarity. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 81\u201391. https:\/\/doi.org\/10.3115\/v1\/S14-2010. https:\/\/www.aclweb.org\/anthology\/S14-2010","DOI":"10.3115\/v1\/S14-2010"},{"key":"2794_CR23","doi-asserted-by":"publisher","unstructured":"Agirre E, Banea C, Cardie C, Cer D, Diab M, Gonzalez-Agirre A, Guo W, Lopez-Gazpio I, Maritxalar M, Mihalcea R, Rigau G, Uria L, Wiebe J (2015) SemEval-2015 task 2: semantic textual similarity, English, Spanish and pilot on interpretability. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 252\u2013263. https:\/\/doi.org\/10.18653\/v1\/S15-2045. https:\/\/www.aclweb.org\/anthology\/S15-2045","DOI":"10.18653\/v1\/S15-2045"},{"key":"2794_CR24","doi-asserted-by":"publisher","unstructured":"Agirre E, Banea C, Cer D, Diab M, Gonzalez-Agirre A, Mihalcea R, Rigau G, Wiebe J (2016) SemEval-2016 Task 1: semantic textual similarity, monolingual and cross-lingual evaluation. Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/S16-1081. https:\/\/www.aclweb.org\/anthology\/S16-1081","DOI":"10.18653\/v1\/S16-1081"},{"key":"2794_CR25","doi-asserted-by":"publisher","unstructured":"Cer D, Diab M, Agirre E, Lopez-Gazpio I, Specia L (2017) SemEval-2017 task 1: semantic textual similarity multilingual and crosslingual focused evaluation. In: Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pp. 1\u201314 . https:\/\/doi.org\/10.18653\/v1\/S17-2001. https:\/\/www.aclweb.org\/anthology\/S17-2001","DOI":"10.18653\/v1\/S17-2001"},{"key":"2794_CR26","doi-asserted-by":"crossref","unstructured":"Marelli M, Menini S, Baroni M, Bentivogli L, Bernardi R, Zamparelli R (2014) A SICK cure for the evaluation of compositional distributional semantic models, pp. 216\u2013223 . http:\/\/www.lrec-conf.org\/proceedings\/lrec2014\/pdf\/363_Paper.pdf","DOI":"10.63317\/39qdhuevzbqa"},{"key":"2794_CR27","doi-asserted-by":"publisher","unstructured":"Muennighoff N, Tazi N, Magne L, Reimers N (2022) Mteb: massive text embedding benchmark. https:\/\/doi.org\/10.48550\/ARXIV.2210.07316. arXiv preprint arXiv:2210.07316","DOI":"10.48550\/ARXIV.2210.07316"},{"key":"2794_CR28","doi-asserted-by":"publisher","unstructured":"Conneau A, Kiela D, Schwenk H, Barrault L, Bordes A (2017) Supervised learning of universal sentence representations from natural language inference data, pp. 670\u2013680. https:\/\/doi.org\/10.18653\/v1\/D17-1070. https:\/\/www.aclweb.org\/anthology\/D17-1070","DOI":"10.18653\/v1\/D17-1070"},{"key":"2794_CR29","doi-asserted-by":"publisher","unstructured":"Cer D, Yang Y, Kong S-Y, Hua N, Limtiaco N, St John R, Constant N, Guajardo-Cespedes M, Yuan S, Tar C, Strope B, Kurzweil R (2018) Universal sentence encoder for English, pp. 169\u2013174. https:\/\/doi.org\/10.18653\/v1\/D18-2029. https:\/\/www.aclweb.org\/anthology\/D18-2029","DOI":"10.18653\/v1\/D18-2029"},{"key":"2794_CR30","doi-asserted-by":"publisher","unstructured":"Henderson M, Casanueva I, Mrk\u0161i\u0107 N, Su P-H, Wen T-H, Vuli\u0107 I (2020) ConveRT: efficient and accurate conversational representations from transformers. In: Cohn T, He Y, Liu Y (eds.) Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 2161\u20132174. Association for Computational Linguistics, Online. https:\/\/doi.org\/10.18653\/v1\/2020.findings-emnlp.196. https:\/\/aclanthology.org\/2020.findings-emnlp.196","DOI":"10.18653\/v1\/2020.findings-emnlp.196"},{"key":"2794_CR31","unstructured":"Mikolov T, Sutskever I, Chen K, Corrado G, Dean J (2013) Distributed representations of words and phrases and their compositionality. In: Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2. NIPS\u201913, pp. 3111\u20133119. Curran Associates Inc., Red Hook, NY, USA"},{"key":"2794_CR32","doi-asserted-by":"publisher","unstructured":"Pagliardini M, Gupta P, Jaggi M (2018) Unsupervised learning of sentence embeddings using compositional n-gram features, pp. 528\u2013540 . https:\/\/doi.org\/10.18653\/v1\/N18-1049. https:\/\/www.aclweb.org\/anthology\/N18-1049","DOI":"10.18653\/v1\/N18-1049"},{"key":"2794_CR33","doi-asserted-by":"publisher","unstructured":"Koshorek O, Cohen A, Mor N, Rotman M, Berant J (2018) Text segmentation as a supervised learning task. In: Walker M, Ji H, Stent A (eds.) Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pp. 469\u2013473. Association for Computational Linguistics, New Orleans, Louisiana. https:\/\/doi.org\/10.18653\/v1\/N18-2075. https:\/\/aclanthology.org\/N18-2075","DOI":"10.18653\/v1\/N18-2075"},{"key":"2794_CR34","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1007\/978-3-319-50496-4_15","volume-title":"Natural language understanding and intelligent applications","author":"L Wang","year":"2016","unstructured":"Wang L, Li S, Xiao X, Lyu Y (2016) Topic segmentation of web documents with automatic cue phrase identification and blstm-cnn. In: Lin C-Y, Xue N, Zhao D, Huang X, Feng Y (eds) Natural language understanding and intelligent applications. Springer, Cham, pp 177\u2013188"},{"key":"2794_CR35","doi-asserted-by":"crossref","unstructured":"Glava G, Somasundaran S (2020) Two-level transformer and auxiliary coherence modeling for improved text segmentation. Association for the Advancement of. Artif Intell AAI","DOI":"10.1609\/aaai.v34i05.6284"},{"key":"2794_CR36","unstructured":"Kiros R, Zhu Y, Salakhutdinov R, Zemel RS, Torralba A, Urtasun R, Fidler S (2015) Skip-thought vectors. In: Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 2. NIPS\u201915, pp. 3294\u20133302. MIT Press, Cambridge, MA, USA"},{"key":"2794_CR37","doi-asserted-by":"publisher","unstructured":"Russell D, Li L, Tian F (2019) Generating text using generative adversarial networks and quick-thought vectors. In: 2019 IEEE 2nd International Conference on Computer and Communication Engineering Technology (CCET), pp. 129\u2013133 . https:\/\/doi.org\/10.1109\/CCET48361.2019.8989352","DOI":"10.1109\/CCET48361.2019.8989352"},{"issue":"12","key":"2794_CR38","doi-asserted-by":"publisher","first-page":"2724","DOI":"10.1109\/TKDE.2017.2754499","volume":"29","author":"Q Wang","year":"2017","unstructured":"Wang Q, Mao Z, Wang B, Guo L (2017) Knowledge graph embedding: a survey of approaches and applications. IEEE Trans Knowl Data Eng 29(12):2724\u20132743","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2794_CR39","doi-asserted-by":"publisher","unstructured":"Zhang Z, Han X, Liu Z, Jiang X, Sun M, Liu Q (2019) ERNIE: enhanced language representation with informative entities. In: Korhonen A, Traum D, M\u00e0rquez L (eds.) Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 1441\u20131451. Association for Computational Linguistics, Florence, Italy. https:\/\/doi.org\/10.18653\/v1\/P19-1139. https:\/\/aclanthology.org\/P19-1139","DOI":"10.18653\/v1\/P19-1139"},{"key":"2794_CR40","doi-asserted-by":"publisher","unstructured":"Gao L, Zhang Y, Han J, Callan J (2021) Scaling deep contrastive learning batch size under memory limited setup. In: Rogers A, Calixto I, Vuli\u0107 I, Saphra N, Kassner N, Camburu OM, Bansal T, Shwartz V (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 316\u2013321. Association for Computational Linguistics, Online. https:\/\/doi.org\/10.18653\/v1\/2021.repl4nlp-1.31. https:\/\/aclanthology.org\/2021.repl4nlp-1.31\/","DOI":"10.18653\/v1\/2021.repl4nlp-1.31"},{"key":"2794_CR41","unstructured":"Muennighoff N (2022) SGPT: GPT sentence embeddings for semantic search. https:\/\/arxiv.org\/abs\/2202.08904"},{"key":"2794_CR42","doi-asserted-by":"publisher","unstructured":"Paranjape B, Lundberg SM, Singh S, Hajishirzi H, Zettlemoyer L, Ribeiro MT (2023) ART: automatic multi-step reasoning and tool-use for large language models. CoRR https:\/\/doi.org\/10.48550\/ARXIV.2303.09014arXiv:abs\/2303.09014","DOI":"10.48550\/ARXIV.2303.09014"},{"key":"2794_CR43","unstructured":"Tang Z, Wang B, Yao T (2022) DPTDR: Deep prompt tuning for dense passage retrieval. In: Calzolari N, Huang C-R, Kim H, Pustejovsky J, Wanner L, Choi K-S, Ryu P-M, Chen H-H, Donatelli L, Ji H, Kurohashi S, Paggio P, Xue N, Kim S, Hahm Y, He Z, Lee TK, Santus E, Bond F, Na S-H (eds.) Proceedings of the 29th International Conference on Computational Linguistics, pp. 1193\u20131202. International Committee on Computational Linguistics, Gyeongju, Republic of Korea. https:\/\/aclanthology.org\/2022.coling-1.103"},{"key":"2794_CR44","doi-asserted-by":"publisher","unstructured":"Ren R, Qu Y, Liu J, Zhao WX, She Q, Wu H, Wang H, Wen J-R (2021) RocketQAv2: a joint training method for dense passage retrieval and passage re-ranking. In: Moens M-F, Huang X, Specia L, Yih SWt (eds.) Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 2825\u20132835. Association for Computational Linguistics, Online and Punta Cana, Dominican Republic. https:\/\/doi.org\/10.18653\/v1\/2021.emnlp-main.224. https:\/\/aclanthology.org\/2021.emnlp-main.224","DOI":"10.18653\/v1\/2021.emnlp-main.224"},{"key":"2794_CR45","unstructured":"Lu Y, Liu Y, Liu J, Shi Y, Huang Z, Sun SFY, Tian H, Wu H, Wang S, Yin D, et al (2022) Ernie-search: Bridging cross-encoder with dual-encoder via self on-the-fly distillation for dense passage retrieval. arXiv preprint arXiv:2205.09153"}],"container-title":["Knowledge and Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-026-02794-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10115-026-02794-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-026-02794-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T09:19:14Z","timestamp":1781601554000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10115-026-02794-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,16]]},"references-count":45,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,12]]}},"alternative-id":["2794"],"URL":"https:\/\/doi.org\/10.1007\/s10115-026-02794-2","relation":{},"ISSN":["0219-1377","0219-3116"],"issn-type":[{"value":"0219-1377","type":"print"},{"value":"0219-3116","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,16]]},"assertion":[{"value":"25 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 September 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 May 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 June 2026","order":4,"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"}},{"value":"The authors declare that they have ethical and informed consent for data used.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical and informed consent for data used"}}],"article-number":"188"}}