{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T15:09:29Z","timestamp":1772809769880,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,11,28]],"date-time":"2022-11-28T00:00:00Z","timestamp":1669593600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42050101"],"award-info":[{"award-number":["42050101"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["20220108"],"award-info":[{"award-number":["20220108"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2021M702991"],"award-info":[{"award-number":["2021M702991"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["WHDYS-2021-014"],"award-info":[{"award-number":["WHDYS-2021-014"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["KLIGIP-2021A01"],"award-info":[{"award-number":["KLIGIP-2021A01"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2020010602012022"],"award-info":[{"award-number":["2020010602012022"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Beijing Key Laboratory of Urban Spatial Information Engineering","award":["42050101"],"award-info":[{"award-number":["42050101"]}]},{"name":"Beijing Key Laboratory of Urban Spatial Information Engineering","award":["20220108"],"award-info":[{"award-number":["20220108"]}]},{"name":"Beijing Key Laboratory of Urban Spatial Information Engineering","award":["2021M702991"],"award-info":[{"award-number":["2021M702991"]}]},{"name":"Beijing Key Laboratory of Urban Spatial Information Engineering","award":["WHDYS-2021-014"],"award-info":[{"award-number":["WHDYS-2021-014"]}]},{"name":"Beijing Key Laboratory of Urban Spatial Information Engineering","award":["KLIGIP-2021A01"],"award-info":[{"award-number":["KLIGIP-2021A01"]}]},{"name":"Beijing Key Laboratory of Urban Spatial Information Engineering","award":["2020010602012022"],"award-info":[{"award-number":["2020010602012022"]}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["42050101"],"award-info":[{"award-number":["42050101"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["20220108"],"award-info":[{"award-number":["20220108"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2021M702991"],"award-info":[{"award-number":["2021M702991"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["WHDYS-2021-014"],"award-info":[{"award-number":["WHDYS-2021-014"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["KLIGIP-2021A01"],"award-info":[{"award-number":["KLIGIP-2021A01"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2020010602012022"],"award-info":[{"award-number":["2020010602012022"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Wuhan Multi-Element Urban Geological Survey Demonstration Project","award":["42050101"],"award-info":[{"award-number":["42050101"]}]},{"name":"Wuhan Multi-Element Urban Geological Survey Demonstration Project","award":["20220108"],"award-info":[{"award-number":["20220108"]}]},{"name":"Wuhan Multi-Element Urban Geological Survey Demonstration Project","award":["2021M702991"],"award-info":[{"award-number":["2021M702991"]}]},{"name":"Wuhan Multi-Element Urban Geological Survey Demonstration Project","award":["WHDYS-2021-014"],"award-info":[{"award-number":["WHDYS-2021-014"]}]},{"name":"Wuhan Multi-Element Urban Geological Survey Demonstration Project","award":["KLIGIP-2021A01"],"award-info":[{"award-number":["KLIGIP-2021A01"]}]},{"name":"Wuhan Multi-Element Urban Geological Survey Demonstration Project","award":["2020010602012022"],"award-info":[{"award-number":["2020010602012022"]}]},{"name":"The Hubei Key Laboratory of Intelligent Geo-Information Processing","award":["42050101"],"award-info":[{"award-number":["42050101"]}]},{"name":"The Hubei Key Laboratory of Intelligent Geo-Information Processing","award":["20220108"],"award-info":[{"award-number":["20220108"]}]},{"name":"The Hubei Key Laboratory of Intelligent Geo-Information Processing","award":["2021M702991"],"award-info":[{"award-number":["2021M702991"]}]},{"name":"The Hubei Key Laboratory of Intelligent Geo-Information Processing","award":["WHDYS-2021-014"],"award-info":[{"award-number":["WHDYS-2021-014"]}]},{"name":"The Hubei Key Laboratory of Intelligent Geo-Information Processing","award":["KLIGIP-2021A01"],"award-info":[{"award-number":["KLIGIP-2021A01"]}]},{"name":"The Hubei Key Laboratory of Intelligent Geo-Information Processing","award":["2020010602012022"],"award-info":[{"award-number":["2020010602012022"]}]},{"name":"Wuhan Science and Technology Plan Project","award":["42050101"],"award-info":[{"award-number":["42050101"]}]},{"name":"Wuhan Science and Technology Plan Project","award":["20220108"],"award-info":[{"award-number":["20220108"]}]},{"name":"Wuhan Science and Technology Plan Project","award":["2021M702991"],"award-info":[{"award-number":["2021M702991"]}]},{"name":"Wuhan Science and Technology Plan Project","award":["WHDYS-2021-014"],"award-info":[{"award-number":["WHDYS-2021-014"]}]},{"name":"Wuhan Science and Technology Plan Project","award":["KLIGIP-2021A01"],"award-info":[{"award-number":["KLIGIP-2021A01"]}]},{"name":"Wuhan Science and Technology Plan Project","award":["2020010602012022"],"award-info":[{"award-number":["2020010602012022"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Toponym recognition, or the challenge of detecting place names that have a similar referent, is involved in a number of activities connected to geographical information retrieval and geographical information sciences. This research focuses on recognizing Chinese toponyms from social media communications. While broad named entity recognition methods are frequently used to locate places, their accuracy is hampered by the many linguistic abnormalities seen in social media posts, such as informal sentence constructions, name abbreviations, and misspellings. In this study, we describe a Chinese toponym identification model based on a hybrid neural network that was created with these linguistic inconsistencies in mind. Our method adds a number of improvements to a standard bidirectional recurrent neural network model to help with location detection in social media messages. We demonstrate the results of a wide-ranging evaluation of the performance of different supervised machine learning methods, which have the natural advantage of avoiding human design features. A set of controlled experiments with four test datasets (one constructed and three public datasets) demonstrates the performance of supervised machine learning that can achieve good results on the task, significantly outperforming seven baseline models.<\/jats:p>","DOI":"10.3390\/ijgi11120598","type":"journal-article","created":{"date-parts":[[2022,11,28]],"date-time":"2022-11-28T07:01:30Z","timestamp":1669618890000},"page":"598","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Geographic Named Entity Recognition by Employing Natural Language Processing and an Improved BERT Model"],"prefix":"10.3390","volume":"11","author":[{"given":"Liufeng","family":"Tao","sequence":"first","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430074, China"},{"name":"Beijing Key Laboratory of Urban Spatial Information Engineering, Beijing 100038, China"}]},{"given":"Zhong","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430074, China"},{"name":"Beijing Key Laboratory of Urban Spatial Information Engineering, Beijing 100038, China"}]},{"given":"Dexin","family":"Xu","sequence":"additional","affiliation":[{"name":"Wuhan Geomatics Institute, Wuhan 430074, China"}]},{"given":"Kai","family":"Ma","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University, Yichang 443002, China"},{"name":"College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China"}]},{"given":"Qinjun","family":"Qiu","sequence":"additional","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430074, China"},{"name":"Beijing Key Laboratory of Urban Spatial Information Engineering, Beijing 100038, China"},{"name":"Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Shengyong","family":"Pan","sequence":"additional","affiliation":[{"name":"Wuhan Zondy Cyber Science & Technology Co., Ltd., Wuhan 430074, China"}]},{"given":"Bo","family":"Huang","sequence":"additional","affiliation":[{"name":"Wuhan Zondy Cyber Science & Technology Co., Ltd., Wuhan 430074, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1145\/2771588","article-title":"Processing social media messages in mass emergency: A survey","volume":"47","author":"Imran","year":"2015","journal-title":"ACM Comput. Surv."},{"key":"ref_2","unstructured":"Silverman, L. (2017, September 12). Facebook, Twitter Replace 911 Calls for Stranded in Houston. Available online: https:\/\/www.npr.org\/sections\/alltechconsidered\/2017\/08\/28\/546831780\/texas-police-and-residents-turn-to-social-media-to-communicateamid-harvey."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1230","DOI":"10.1080\/17538947.2019.1574316","article-title":"Deep learning for real-time social media text classification for situation awareness\u2014Using hurricanes Sandy, Harvey, and Irma as case studies","volume":"12","author":"Yu","year":"2019","journal-title":"Int. J. Digit. Earth"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"714","DOI":"10.1080\/13658816.2018.1458986","article-title":"A natural language processing and geospatial clustering framework for harvesting local place names from geotagged housing advertisements","volume":"33","author":"Hu","year":"2018","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Freire, N., Borbinha, J., Calado, P., and Martins, B. (2011, January 13\u201317). A metadata geoparsing system for place name recognition and resolution in metadata records. Proceedings of the 11th International ACM\/IEEE Joint Conference on Digital Libraries, Ottawa, ON, Canada.","DOI":"10.1145\/1998076.1998140"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1007\/s10707-012-0173-8","article-title":"An algorithm for local geoparsing of microtext","volume":"17","author":"Gelernter","year":"2013","journal-title":"Geoinformatica"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1007\/s10579-017-9385-8","article-title":"What\u2019s missing in geographical parsing?","volume":"52","author":"Gritta","year":"2018","journal-title":"Lang. Resour. Eval."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1080\/13658810701626343","article-title":"Geographical information retrieval","volume":"22","author":"Jones","year":"2008","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1561\/1500000034","article-title":"Geographic Information Retrieval: Progress and Challenges in Spatial Search of Text","volume":"12","author":"Purves","year":"2018","journal-title":"Found. Trends\u00ae Inf. Retr."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Derczynski, L., Nichols, E., Van Erp, M., and Limsopatham, N. (2017, January 7). Results of the WNUT2017 shared task on novel and emerging entity recognition. Proceedings of the Third Workshop on Noisy User-Generated Text, Copenhagen, Denmark.","DOI":"10.18653\/v1\/W17-4418"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Li, H., Wang, M., Baldwin, T., Tomko, M., and Vasardani, M. (2019, January 6\u20137). UniMelb at SemEval-2019 Task 12: Multi-model combination for toponym resolution. Proceedings of the 13th International Workshop on Semantic Evaluation, Minneapolis, MN, USA.","DOI":"10.18653\/v1\/S19-2231"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1007\/s12145-019-00390-3","article-title":"BiLSTM-CRF for geological named entity recognition from the geoscience literature","volume":"12","author":"Qiu","year":"2019","journal-title":"Earth Sci. Inform."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"931","DOI":"10.1029\/2019EA000610","article-title":"GNER: A generative model for geological named entity recognition without labeled data using deep learning","volume":"6","author":"Qiu","year":"2019","journal-title":"Earth Space Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1080\/13658816.2017.1390119","article-title":"Toponym matching through deep neural networks","volume":"32","author":"Santos","year":"2018","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1393","DOI":"10.1111\/tgis.12579","article-title":"Enhancing spatial and textual analysis with EUPEG: An extensible and unified platform for evaluating geoparsers","volume":"23","author":"Wang","year":"2019","journal-title":"Trans. GIS"},{"key":"ref_16","unstructured":"Herskovits, A. (1986). Language and Spatial Cognition: An interdisciplinary Study of Prepositions in English, Cambridge University Press."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Talmy, L. (2000). Toward a Cognitive Semantics: Concept Structuring Systems, The MIT Press.","DOI":"10.7551\/mitpress\/6847.001.0001"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1087","DOI":"10.1080\/13658816.2018.1432861","article-title":"Context-aware automated interpretation of elaborate natural language descriptions of location through learning from empirical data","volume":"32","author":"Stock","year":"2018","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_19","unstructured":"Cohen, W., Ravikumar, P., and Fienberg, S. (2003, January 24\u201327). A comparison of string distance metrics for namematching tasks. In Proceedings of KDD Workshop on Data Cleaning and Object Consolidation, Washington, DC, USA."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Moreau, E., Yvon, F., and Capp, E.O. (2008, January 18\u201322). Robust similarity measures for named entities matching. Proceedings of the International Conference on Computational Linguistics, Manchester, UK.","DOI":"10.3115\/1599081.1599156"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"913","DOI":"10.1080\/17538947.2017.1371253","article-title":"Learning to combine multiple string similarity metrics for effective toponym matching","volume":"11","author":"Santos","year":"2018","journal-title":"Int. J. Digit. Earth"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1007\/s12145-021-00756-6","article-title":"Extraction of temporal information from social media messages using the BERT model","volume":"15","author":"Ma","year":"2022","journal-title":"Earth Sci. Inform."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1111\/tgis.12887","article-title":"Spatially oriented convolutional neural network for spatial relation extraction from natural language texts","volume":"26","author":"Qiu","year":"2021","journal-title":"Trans. GIS"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1111\/tgis.12887","article-title":"Spatially oriented convolutional neural network for spatial relation extraction from natural language texts","volume":"26","author":"Qiu","year":"2022","journal-title":"Trans. GIS"},{"key":"ref_25","unstructured":"Devlin, J., Chang, M.W., Lee, K., and Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv."},{"key":"ref_26","first-page":"9","article-title":"Language models are unsupervised multitask learners","volume":"1","author":"Radford","year":"2019","journal-title":"OpenAI blog"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ling, W., Dyer, C., Black, A.W., and Trancoso, I. (, January May\u2013June). Two\/too simple adaptations of word2vec for syntax problems. Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Denver, CO, USA.","DOI":"10.3115\/v1\/N15-1142"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"e2021EA002166","DOI":"10.1029\/2021EA002166","article-title":"Chinese Named Entity Recognition in the Geoscience Domain Based on BERT","volume":"9","author":"Lv","year":"2022","journal-title":"Earth Space Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/s12145-021-00695-2","article-title":"What is this article about? Generative summarization with the BERT model in the geosciences domain","volume":"15","author":"Ma","year":"2021","journal-title":"Earth Sci. Inform."},{"key":"ref_30","unstructured":"Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., and Soricut, R. (2019). Albert: A lite bert for self-supervised learning of language representations. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Graves, A. (2012). Long short-term memory. Supervised Sequence Labelling with Recurrent Neural Networks, Springer.","DOI":"10.1007\/978-3-642-24797-2"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cageo.2018.08.006","article-title":"DGeoSegmenter: A dictionary-based Chinese word segmenter for the geoscience domain","volume":"121","author":"Qiu","year":"2018","journal-title":"Comput. Geosci."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"5195","DOI":"10.1007\/s10586-017-1146-3","article-title":"Named entity recognition based on conditional random fields","volume":"22","author":"Song","year":"2017","journal-title":"Clust. Comput."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"105830","DOI":"10.1016\/j.compag.2020.105830","article-title":"Chinese agricultural diseases and pests named entity recognition with multi-scale local context features and self-attention mechanism","volume":"179","author":"Guo","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_36","unstructured":"Leitner, E., Rehm, G., and Moreno-Schneider, J. (2020). A dataset of german legal documents for named entity recognition. arXiv."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Wang, S., Zhang, X., Ye, P., and Du, M. (2018). Deep Belief Networks Based Toponym Recognition for Chinese Text. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7060217"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Wang, X., Ma, C., Zheng, H., Liu, C., Xie, P., Li, L., and Si, L. (2019, January 6\u20137). DM NLP at SemEval 2018 Task 12: A pipeline system for toponym resolution. Proceedings of the 13th International Workshop on Semantic Evaluation, Minneapolis, MN, USA.","DOI":"10.18653\/v1\/S19-2156"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1111\/tgis.12627","article-title":"NeuroTPR: A neuro-net toponym recognition model for extracting locations from social media messages","volume":"24","author":"Wang","year":"2020","journal-title":"Trans. GIS"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1007\/s10109-022-00375-9","article-title":"Chinese toponym recognition with variant neural structures from social media messages based on BERT methods","volume":"24","author":"Ma","year":"2022","journal-title":"J. Geogr. Syst."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1256","DOI":"10.1111\/tgis.12902","article-title":"ChineseTR: A weakly supervised toponym recognition architecture based on automatic training data generator and deep neural network","volume":"26","author":"Qiu","year":"2022","journal-title":"Trans. GIS"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"16259","DOI":"10.1109\/JIOT.2022.3150967","article-title":"GazPNE2: A General Place Name Extractor for Microblogs Fusing Gazetteers and Pretrained Transformer Models","volume":"9","author":"Hu","year":"2022","journal-title":"IEEE Internet Things J."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/11\/12\/598\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:28:09Z","timestamp":1760146089000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/11\/12\/598"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,28]]},"references-count":42,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["ijgi11120598"],"URL":"https:\/\/doi.org\/10.3390\/ijgi11120598","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,28]]}}}