{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T07:01:11Z","timestamp":1780297271030,"version":"3.54.0"},"reference-count":48,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T00:00:00Z","timestamp":1778544000000},"content-version":"vor","delay-in-days":11,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100003819","name":"Natural Science Foundation of Hubei Province","doi-asserted-by":"publisher","award":["2023AFA006"],"award-info":[{"award-number":["2023AFA006"]}],"id":[{"id":"10.13039\/501100003819","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42071382"],"award-info":[{"award-number":["42071382"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Transactions in GIS"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Geographic knowledge graph representation learning (GeoKGRL) seeks to embed entities and relationships within a geographic knowledge graph (GeoKG) into a vector space, enabling efficient computation, retrieval, and inference on large\u2010scale datasets. However, existing GeoKGRL methods often overlook the hierarchical nature of geographic entities such as transboundary watersheds and nested administrative regions, leading to inadequate modeling of cross\u2010hierarchy distributional divergence. Consequently, the learned representations may not fully capture the inherent geographic semantics of geographic entities. To address this challenge, we propose a Hierarchical Adaptive Meta\u2010Learning (HAML) approach for enhancing GeoKGRL. Specifically, HAML introduces a region\u2010guided subgraph sampling strategy and a subgraph learning module to effectively capture entity and relationship characteristics across different geographic scales. Additionally, a hierarchical optimization module is designed to refine geographic knowledge representations through a \u201cbottom\u2010up update, top\u2010down feedback\u201d mechanism. By dynamically extracting local structural patterns at varying geographic scales, HAML enhances representation robustness, while a global meta\u2010optimizer facilitates cross\u2010hierarchy learning, mitigating distributional divergence between hierarchical embeddings. Extensive experiments conducted on OpenStreetMap\u2010based datasets demonstrate that HAML significantly outperforms baseline methods. These results highlight the effectiveness of HAML in explicit modeling of GeoKG structures, offering valuable insights for both GeoKG research and broader geographic applications.<\/jats:p>","DOI":"10.1111\/tgis.70245","type":"journal-article","created":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T03:34:31Z","timestamp":1778643271000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Hierarchical Adaptive Meta\u2010Learning for Geographic Knowledge Graph Representation Learning"],"prefix":"10.1111","volume":"30","author":[{"given":"Hong","family":"Yao","sequence":"first","affiliation":[{"name":"School of Computer Science China University of Geosciences  Hubei China"},{"name":"State Key Laboratory of Geomicrobiology and Environmental Changes China University of Geosciences  Hubei China"},{"name":"Hubei Key Laboratory of Intelligent Geo\u2010Information Processing China University of Geosciences  Hubei China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zi","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Future Technology China University of Geosciences  Hubei China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Renyao","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science China University of Geosciences  Hubei China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-1929-9647","authenticated-orcid":false,"given":"Li","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Computer Science China University of Geosciences  Hubei China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shengwen","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science China University of Geosciences  Hubei China"},{"name":"State Key Laboratory of Geomicrobiology and Environmental Changes China University of Geosciences  Hubei China"},{"name":"Hubei Key Laboratory of Intelligent Geo\u2010Information Processing China University of Geosciences  Hubei China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2721-3626","authenticated-orcid":false,"given":"Haijun","family":"Song","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Geomicrobiology and Environmental Changes China University of Geosciences  Hubei China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingzhong","family":"Liang","sequence":"additional","affiliation":[{"name":"School of Computer Science China University of Geosciences  Hubei China"},{"name":"Hubei Key Laboratory of Intelligent Geo\u2010Information Processing China University of Geosciences  Hubei China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,5,12]]},"reference":[{"key":"e_1_2_11_2_1","first-page":"546","volume-title":"Advances in Neural Information Processing Systems","author":"Baek J.","year":"2020"},{"key":"e_1_2_11_3_1","volume-title":"Advances in Neural Information Processing Systems, 26","author":"Bordes A.","year":"2013"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1431"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11573"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.118806"},{"key":"e_1_2_11_7_1","first-page":"1126","volume-title":"International Conference on Machine Learning","author":"Finn C.","year":"2017"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.1088\/1755-1315\/252\/5\/052161"},{"key":"e_1_2_11_9_1","first-page":"221","volume-title":"Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics","author":"Hao Y.","year":"2017"},{"issue":"9","key":"e_1_2_11_10_1","first-page":"5149","article-title":"Meta\u2010Learning in Neural Networks: A Survey","volume":"44","author":"Hospedales T.","year":"2021","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.3390\/ijgi11090493"},{"key":"e_1_2_11_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.05.114"},{"key":"e_1_2_11_13_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/P15-1067"},{"key":"e_1_2_11_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.121446"},{"key":"e_1_2_11_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2022.11.019"},{"key":"e_1_2_11_16_1","doi-asserted-by":"publisher","DOI":"10.1080\/13658816.2023.2239307"},{"key":"e_1_2_11_17_1","doi-asserted-by":"publisher","DOI":"10.3390\/ijgi14010018"},{"key":"e_1_2_11_18_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v29i1.9491"},{"key":"e_1_2_11_19_1","first-page":"4279","volume-title":"IEEE Transactions on Neural Networks and Learning Systems","author":"Liu R.","year":"2024"},{"key":"e_1_2_11_20_1","first-page":"4276","volume-title":"Proceedings of the AAAI conference on artificial intelligence","author":"Lu Y.","year":"2021"},{"key":"e_1_2_11_21_1","volume-title":"A Simple Neural Attentive Meta\u2010Learner. International Conference on Learning Representations","author":"Mishra N.","year":"2018"},{"key":"e_1_2_11_22_1","first-page":"2554","volume-title":"International Conference on Machine Learning","author":"Munkhdalai T.","year":"2017"},{"key":"e_1_2_11_23_1","first-page":"3104482","volume-title":"ICML","author":"Nickel M.","year":"2011"},{"key":"e_1_2_11_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462925"},{"key":"e_1_2_11_25_1","first-page":"32","volume-title":"Advances in Neural Information Processing Systems","author":"Paszke A.","year":"2019"},{"key":"e_1_2_11_26_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-023-10465-9"},{"key":"e_1_2_11_27_1","doi-asserted-by":"publisher","DOI":"10.3390\/ijgi8060254"},{"key":"e_1_2_11_28_1","doi-asserted-by":"publisher","DOI":"10.2991\/icammce-18.2018.66"},{"key":"e_1_2_11_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2008.2005605"},{"key":"e_1_2_11_30_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-93417-4_38"},{"key":"e_1_2_11_31_1","unstructured":"Sun Z. Z. H.Deng J. Y.Nie andJ.Tang.2019. \u201cRotate: Knowledge Graph Embedding by Relational Rotation in Complex Space.\u201d arXiv Preprint arXiv:1902.10197."},{"key":"e_1_2_11_32_1","first-page":"2071","volume-title":"International Conference on Machine Learning","author":"Trouillon T.","year":"2016"},{"key":"e_1_2_11_33_1","volume-title":"International Conference on Learning Representations","author":"Vashishth S.","year":"2020"},{"key":"e_1_2_11_34_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cobeha.2021.01.002"},{"key":"e_1_2_11_35_1","volume-title":"ICLR Workshop on Representation Learning on Graphs and Manifolds","author":"Wang M. Y.","year":"2019"},{"key":"e_1_2_11_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2017.2754499"},{"key":"e_1_2_11_37_1","doi-asserted-by":"publisher","DOI":"10.3390\/ijgi8040184"},{"key":"e_1_2_11_38_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v28i1.8870"},{"key":"e_1_2_11_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/ITAIC49862.2020.9339104"},{"key":"e_1_2_11_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"e_1_2_11_41_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-1223"},{"key":"e_1_2_11_42_1","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Yang B.","year":"2015"},{"key":"e_1_2_11_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939673"},{"key":"e_1_2_11_44_1","doi-asserted-by":"publisher","DOI":"10.1111\/tgis.12985"},{"key":"e_1_2_11_45_1","first-page":"3065","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Zhang Z.","year":"2020"},{"issue":"3","key":"e_1_2_11_46_1","first-page":"2641","article-title":"Hierarchical Representation Learning for Attributed Networks","volume":"35","author":"Zhao S.","year":"2021","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_2_11_47_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11430-020-9750-4"},{"key":"e_1_2_11_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583239"},{"key":"e_1_2_11_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583305"}],"container-title":["Transactions in GIS"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/tgis.70245","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1111\/tgis.70245","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/tgis.70245","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T06:13:40Z","timestamp":1780294420000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/tgis.70245"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5]]},"references-count":48,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,5]]}},"alternative-id":["10.1111\/tgis.70245"],"URL":"https:\/\/doi.org\/10.1111\/tgis.70245","archive":["Portico"],"relation":{},"ISSN":["1361-1682","1467-9671"],"issn-type":[{"value":"1361-1682","type":"print"},{"value":"1467-9671","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5]]},"assertion":[{"value":"2025-06-11","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-03-04","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-05-12","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70245"}}