{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T05:10:34Z","timestamp":1775538634078,"version":"3.50.1"},"reference-count":67,"publisher":"Association for Computing Machinery (ACM)","issue":"6","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62032003,62425203"],"award-info":[{"award-number":["62032003,62425203"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Beijing Natural Science Foundation","award":["L253005"],"award-info":[{"award-number":["L253005"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. ACM Manag. Data"],"published-print":{"date-parts":[[2025,12,4]]},"abstract":"<jats:p>Geospatial Knowledge Graphs (KGs) are widely used data structures that integrate rich knowledge from multi-source databases and play a crucial role in applications such as data retrieval and urban management. However, existing methods for Geospatial Knowledge Graph Completion (KGC) heavily rely on extensive labeled data and lack the ability to direct inference on new unlabeled geospatial databases and thus limiting their practical deployment. To address this limitation, this paper first formalizes a novel zero-shot transfer scenario and then proposes an innovative geospatial multimodal large language model framework capable of efficient Geospatial KGC with robust zero-shot generalization capabilities. Specifically, to enable effective direct inference under significant data discrepancies inherent in zero-shot scenarios, we introduce large language models (LLMs) into the geospatial KGC problem for the first time and redefine the multimodal data processing paradigm for geospatial LLMs. Next, to overcome the challenge that LLMs cannot directly handle geospatial data, we innovatively propose a Pretrain Geospatial Encoder that performs self-supervised pretraining exclusively on spatial data. Additionally, to integrate geospatial and textual modalities, we design an adaptation component that injects geospatial features into the LLMs and introduce a multi-task fine-tuning procedure. Lastly, to ensure robustness across multi-target domain scenarios, we present an implicit data alignment strategy based on adversarial learning. Extensive evaluations conducted on four real-world datasets demonstrate that our method significantly outperforms state-of-the-art approaches in terms of accuracy and robustness.<\/jats:p>","DOI":"10.1145\/3769796","type":"journal-article","created":{"date-parts":[[2025,12,6]],"date-time":"2025-12-06T04:32:13Z","timestamp":1764995533000},"page":"1-28","source":"Crossref","is-referenced-by-count":0,"title":["GeoKGM: A Multimodal Large Language Model for Zero-Shot Knowledge Graph Completion in Geospatial Databases"],"prefix":"10.1145","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-8896-4222","authenticated-orcid":false,"given":"Zhihan","family":"Zheng","sequence":"first","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6721-065X","authenticated-orcid":false,"given":"Haitao","family":"Yuan","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-0476-6174","authenticated-orcid":false,"given":"Minxiao","family":"Chen","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-3933-1662","authenticated-orcid":false,"given":"Nan","family":"Jiang","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-0307-1853","authenticated-orcid":false,"given":"Haoning","family":"Wang","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7245-1298","authenticated-orcid":false,"given":"Shangguang","family":"Wang","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2025,12,5]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"2025. GeoNames. https:\/\/www.geonames.org\/"},{"key":"e_1_2_1_2_1","unstructured":"2025. OpenAI. https:\/\/openai.com\/"},{"key":"e_1_2_1_3_1","unstructured":"2025. OpenStreetMap. https:\/\/www.openstreetmap.org"},{"key":"e_1_2_1_4_1","unstructured":"2025. Yelp. https:\/\/www.yelp.com\/developers"},{"key":"e_1_2_1_5_1","volume-title":"Qingheng Zhang, Wei Hu, and Chengkai Li.","author":"Akrami Farahnaz","year":"2020","unstructured":"Farahnaz Akrami, Mohammed Samiul Saeef, Qingheng Zhang, Wei Hu, and Chengkai Li. 2020. Realistic Re-evaluation of Knowledge Graph Completion Methods: An Experimental Study. In SIGMOD, David Maier, Rachel Pottinger, AnHai Doan, Wang-Chiew Tan, Abdussalam Alawini, and Hung Q. Ngo (Eds.). ACM, 1995-2010."},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2022.3198972"},{"key":"e_1_2_1_7_1","first-page":"3061","article-title":"Geospatial Entity Resolution. In WWW, Fr\u00e9d\u00e9rique Laforest, Rapha\u00ebl Troncy, Elena Simperl, Deepak Agarwal, Aristides Gionis, Ivan Herman, and Lionel M\u00e9dini (Eds.)","author":"Balsebre Pasquale","year":"2022","unstructured":"Pasquale Balsebre, Dezhong Yao, Gao Cong, and Zhen Hai. 2022. Geospatial Entity Resolution. In WWW, Fr\u00e9d\u00e9rique Laforest, Rapha\u00ebl Troncy, Elena Simperl, Deepak Agarwal, Aristides Gionis, Ivan Herman, and Lionel M\u00e9dini (Eds.). ACM, 3061-3070.","journal-title":"ACM"},{"key":"e_1_2_1_8_1","first-page":"1","article-title":"Mining Geospatial Relationships from Text","volume":"1","author":"Balsebre Pasquale","year":"2023","unstructured":"Pasquale Balsebre, Dezhong Yao, Gao Cong, Weiming Huang, and Zhen Hai. 2023. Mining Geospatial Relationships from Text. In SIGMOD, Vol. 1. 93:1-93:26.","journal-title":"SIGMOD"},{"key":"e_1_2_1_9_1","unstructured":"Antoine Bordes Nicolas Usunier Alberto Garc\u00eda-Dur\u00e1n Jason Weston and Oksana Yakhnenko. 2013. Translating Embeddings for Modeling Multi-relational Data. In NIPS Christopher J. C. Burges L\u00e9on Bottou Zoubin Ghahramani and Kilian Q. Weinberger (Eds.). 2787-2795."},{"key":"e_1_2_1_10_1","first-page":"281","article-title":"Urban Traffic Accident Risk Prediction Revisited: Regionality, Proximity, Similarity and Sparsity. In CIKM, Edoardo Serra and Francesca Spezzano (Eds.)","author":"Chen Minxiao","year":"2024","unstructured":"Minxiao Chen, Haitao Yuan, Nan Jiang, Zhifeng Bao, and Shangguang Wang. 2024. Urban Traffic Accident Risk Prediction Revisited: Regionality, Proximity, Similarity and Sparsity. In CIKM, Edoardo Serra and Francesca Spezzano (Eds.). ACM, 281-290.","journal-title":"ACM"},{"key":"e_1_2_1_11_1","first-page":"4285","article-title":"S-MGHSTN: Towards An Effective Streaming Traffic Accident Risk Prediction Framework","volume":"37","author":"Chen Minxiao","year":"2025","unstructured":"Minxiao Chen, Haitao Yuan, Nan Jiang, Zhihan Zheng, Zhifeng Bao, Ao Zhou, Jiaxin Jiang, and Shangguang Wang. 2025. S-MGHSTN: Towards An Effective Streaming Traffic Accident Risk Prediction Framework. In TKDE, Vol. 37. 4285-4298.","journal-title":"TKDE"},{"key":"e_1_2_1_12_1","first-page":"1","article-title":"RLOMM: An Efficient and Robust Online Map Matching Framework with Reinforcement Learning","volume":"3","author":"Chen Minxiao","year":"2025","unstructured":"Minxiao Chen, Haitao Yuan, Nan Jiang, Zhihan Zheng, Sai Wu, Ao Zhou, and Shangguang Wang. 2025. RLOMM: An Efficient and Robust Online Map Matching Framework with Reinforcement Learning. In SIGMOD, Vol. 3. 209:1-209:26.","journal-title":"SIGMOD"},{"key":"e_1_2_1_13_1","first-page":"1563","article-title":"NOUS: Construction and Querying of Dynamic Knowledge Graphs","author":"Choudhury Sutanay","year":"2017","unstructured":"Sutanay Choudhury, Khushbu Agarwal, Sumit Purohit, Baichuan Zhang, Meg Pirrung, William P. Smith, and Mathew Thomas. 2017. NOUS: Construction and Querying of Dynamic Knowledge Graphs. In ICDE. IEEE Computer Society, 1563-1565.","journal-title":"ICDE. IEEE Computer Society"},{"key":"e_1_2_1_14_1","first-page":"1","article-title":"PaLM: Scaling Language Modeling with Pathways","volume":"24","author":"Chowdhery Aakanksha","year":"2023","unstructured":"Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, Parker Schuh, Kensen Shi, Sasha Tsvyashchenko, Joshua Maynez, Abhishek Rao, Parker Barnes, Yi Tay, Noam Shazeer, Vinodkumar Prabhakaran, Emily Reif, Nan Du, Ben Hutchinson, Reiner Pope, James Bradbury, Jacob Austin, Michael Isard, Guy Gur-Ari, Pengcheng Yin, Toju Duke, Anselm Levskaya, Sanjay Ghemawat, Sunipa Dev, Henryk Michalewski, Xavier Garcia, Vedant Misra, Kevin Robinson, Liam Fedus, Denny Zhou, Daphne Ippolito, David Luan, Hyeontaek Lim, Barret Zoph, Alexander Spiridonov, Ryan Sepassi, David Dohan, Shivani Agrawal, Mark Omernick, Andrew M. Dai, Thanumalayan Sankaranarayana Pillai, Marie Pellat, Aitor Lewkowycz, Erica Moreira, Rewon Child, Oleksandr Polozov, Katherine Lee, Zongwei Zhou, Xuezhi Wang, Brennan Saeta, Mark Diaz, Orhan Firat, Michele Catasta, Jason Wei, Kathy Meier-Hellstern, Douglas Eck, Jeff Dean, Slav Petrov, and Noah Fiedel. 2023. PaLM: Scaling Language Modeling with Pathways. In J. Mach. Learn. Res., Vol. 24. 240:1-240:113.","journal-title":"J. Mach. Learn. Res."},{"key":"e_1_2_1_15_1","volume-title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In NAACL-HLT","author":"Devlin Jacob","year":"2019","unstructured":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In NAACL-HLT, Jill Burstein, Christy Doran, and Thamar Solorio (Eds.). Association for Computational Linguistics, 4171-4186."},{"key":"e_1_2_1_16_1","first-page":"4475","article-title":"WorldKG: AWorld-Scale Geographic Knowledge Graph. In CIKM, Gianluca Demartini, Guido Zuccon, J. Shane Culpepper, Zi Huang, and Hanghang Tong (Eds.)","author":"Dsouza Alishiba","year":"2021","unstructured":"Alishiba Dsouza, Nicolas Tempelmeier, Ran Yu, Simon Gottschalk, and Elena Demidova. 2021. WorldKG: AWorld-Scale Geographic Knowledge Graph. In CIKM, Gianluca Demartini, Guido Zuccon, J. Shane Culpepper, Zi Huang, and Hanghang Tong (Eds.). ACM, 4475-4484.","journal-title":"ACM"},{"key":"e_1_2_1_17_1","first-page":"2750","article-title":"Combining Small Language Models and Large Language Models for Zero-Shot NL2SQL","volume":"17","author":"Fan Ju","year":"2024","unstructured":"Ju Fan, Zihui Gu, Songyue Zhang, Yuxin Zhang, Zui Chen, Lei Cao, Guoliang Li, Samuel Madden, Xiaoyong Du, and Nan Tang. 2024. Combining Small Language Models and Large Language Models for Zero-Shot NL2SQL. In VLDB, Vol. 17. 2750-2763.","journal-title":"VLDB"},{"key":"e_1_2_1_18_1","doi-asserted-by":"crossref","unstructured":"Yuchen Guo Guiguang Ding Jungong Han and Yue Gao. 2017. Synthesizing Samples for Zero-shot Learning. In IJCAI Carles Sierra (Ed.). ijcai.org 1774-1780.","DOI":"10.24963\/ijcai.2017\/246"},{"key":"e_1_2_1_19_1","first-page":"2884","article-title":"Data Lakes Empowered by Knowledge Graph Technologies. In SIGMOD, Guoliang Li, Zhanhuai Li, Stratos Idreos, and Divesh Srivastava (Eds.)","author":"Helal Ahmed","year":"2021","unstructured":"Ahmed Helal. 2021. Data Lakes Empowered by Knowledge Graph Technologies. In SIGMOD, Guoliang Li, Zhanhuai Li, Stratos Idreos, and Divesh Srivastava (Eds.). ACM, 2884-2886.","journal-title":"ACM"},{"key":"e_1_2_1_20_1","volume-title":"Kai Zhang, Chongyi Wang, Yuan Yao, Chenyang Zhao, Jie Zhou, Jie Cai, Zhongwu Zhai, Ning Ding, Chao Jia, Guoyang Zeng, Dahai Li, Zhiyuan Liu, and Maosong Sun.","author":"Hu Shengding","year":"2024","unstructured":"Shengding Hu, Yuge Tu, Xu Han, Chaoqun He, Ganqu Cui, Xiang Long, Zhi Zheng, Yewei Fang, Yuxiang Huang, Weilin Zhao, Xinrong Zhang, Zhen Leng Thai, Kai Zhang, Chongyi Wang, Yuan Yao, Chenyang Zhao, Jie Zhou, Jie Cai, Zhongwu Zhai, Ning Ding, Chao Jia, Guoyang Zeng, Dahai Li, Zhiyuan Liu, and Maosong Sun. 2024. MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies. In CoRR, Vol. abs\/2404.06395."},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1080\/13658816.2023.2266495"},{"key":"e_1_2_1_22_1","first-page":"1","article-title":"Deep Active Alignment of Knowledge Graph Entities and Schemata","volume":"1","author":"Huang Jiacheng","year":"2023","unstructured":"Jiacheng Huang, Zequn Sun, Qijin Chen, Xiaozhou Xu, Weijun Ren, and Wei Hu. 2023. Deep Active Alignment of Knowledge Graph Entities and Schemata. In SIGMOD, Vol. 1. 159:1-159:26.","journal-title":"SIGMOD"},{"key":"e_1_2_1_23_1","first-page":"1","article-title":"Shortest Paths Discovery in Uncertain Networks via Transfer Learning","volume":"1","author":"Huang Shixun","year":"2023","unstructured":"Shixun Huang and Zhifeng Bao. 2023. Shortest Paths Discovery in Uncertain Networks via Transfer Learning. In SIGMOD, Vol. 1. 141:1-141:25.","journal-title":"SIGMOD"},{"key":"e_1_2_1_24_1","volume-title":"Arabnia","author":"Iman Mohammadreza","year":"2022","unstructured":"Mohammadreza Iman, Khaled Rasheed, and Hamid R. Arabnia. 2022. A Review of Deep Transfer Learning and Recent Advancements. In CoRR, Vol. abs\/2201.09679."},{"key":"e_1_2_1_25_1","first-page":"465","article-title":"Deep Transfer Learning for Multi-source Entity Linkage via Domain Adaptation","volume":"15","author":"Jin Di","year":"2021","unstructured":"Di Jin, Bunyamin Sisman, Hao Wei, Xin Luna Dong, and Danai Koutra. 2021. Deep Transfer Learning for Multi-source Entity Linkage via Domain Adaptation. In VLDB, Vol. 15. 465-477.","journal-title":"VLDB"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-30796-7_12"},{"key":"e_1_2_1_27_1","volume-title":"Kingma and Jimmy Ba","author":"Diederik","year":"2015","unstructured":"Diederik P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In ICLR, Yoshua Bengio and Yann LeCun (Eds.)."},{"key":"e_1_2_1_28_1","volume-title":"Kipf and Max Welling","author":"Thomas","year":"2017","unstructured":"Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In ICLR. OpenReview.net."},{"key":"e_1_2_1_29_1","volume-title":"Hoi","author":"Li Junnan","year":"2023","unstructured":"Junnan Li, Dongxu Li, Silvio Savarese, and Steven C. H. Hoi. 2023. BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models. In ICML (Proceedings of Machine Learning Research, Vol. 202), Andreas Krause, Emma Brunskill, Kyunghyun Cho, Barbara Engelhardt, Sivan Sabato, and Jonathan Scarlett (Eds.). PMLR, 19730-19742."},{"key":"e_1_2_1_30_1","volume-title":"UrbanGPT: Spatio-Temporal Large Language Models","author":"Li Zhonghang","unstructured":"Zhonghang Li, Lianghao Xia, Jiabin Tang, Yong Xu, Lei Shi, Long Xia, Dawei Yin, and Chao Huang. 2024. UrbanGPT: Spatio-Temporal Large Language Models. In SIGKDD, Ricardo Baeza-Yates and Francesco Bonchi (Eds.). ACM, 5351- 5362."},{"key":"e_1_2_1_31_1","volume-title":"CoRR","author":"Liu Chang","unstructured":"Chang Liu, Chen Gao, Depeng Jin, and Yong Li. 2021. Improving Location Recommendation with Urban Knowledge Graph. In CoRR, Vol. abs\/2111.01013."},{"key":"e_1_2_1_32_1","first-page":"1","article-title":"Incremental Tabular Learning on Heterogeneous Feature Space","volume":"1","author":"Liu Hanmo","year":"2023","unstructured":"Hanmo Liu, Shimin Di, and Lei Chen. 2023. Incremental Tabular Learning on Heterogeneous Feature Space. In SIGMOD, Vol. 1. 18:1-18:18.","journal-title":"SIGMOD"},{"key":"e_1_2_1_33_1","volume-title":"Sustainability","author":"Liu Junnan","year":"2005","unstructured":"Junnan Liu, Haiyan Liu, Xiaohui Chen, Xuan Guo, Qingbo Zhao, Jia Li, Lei Kang, and Jianxiang Liu. 2021. A heterogeneous geospatial data retrieval method using knowledge graph. In Sustainability, Vol. 13. MDPI, 2005."},{"key":"e_1_2_1_34_1","volume-title":"Proceedings of Workshops at the 50th International Conference on Very Large Data Bases, VLDB. VLDB.org.","author":"Liu Xinfu","year":"2024","unstructured":"Xinfu Liu, Yirui Wu, Yuting Zhou, Junyang Chen, Huan Wang, Ye Liu, and Shaohua Wan. 2024. Enhancing Large Language Models with Multimodality and Knowledge Graphs for Hallucination-free Open-set Object Recognition. In Proceedings of Workshops at the 50th International Conference on Very Large Data Bases, VLDB. VLDB.org."},{"key":"e_1_2_1_35_1","first-page":"1","article-title":"KnowSite: Leveraging Urban Knowledge Graph for Site Selection. In SIGSPATIAL, Matthias Renz and Mario A. Nascimento (Eds.)","volume":"90","author":"Liu Yu","year":"2023","unstructured":"Yu Liu, Jingtao Ding, and Yong Li. 2023. KnowSite: Leveraging Urban Knowledge Graph for Site Selection. In SIGSPATIAL, Matthias Renz and Mario A. Nascimento (Eds.). ACM, 90:1-90:12.","journal-title":"ACM"},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2868685"},{"key":"e_1_2_1_37_1","volume-title":"Learning Transferable Features with Deep Adaptation Networks. In ICML (JMLR Workshop and Conference Proceedings","volume":"105","author":"Long Mingsheng","unstructured":"Mingsheng Long, Yue Cao, Jianmin Wang, and Michael I. Jordan. 2015. Learning Transferable Features with Deep Adaptation Networks. In ICML (JMLR Workshop and Conference Proceedings, Vol. 37), Francis R. Bach and David M. Blei (Eds.). JMLR.org, 97-105."},{"key":"e_1_2_1_38_1","unstructured":"Ilya Loshchilov and Frank Hutter. 2019. Decoupled Weight Decay Regularization. In ICLR. OpenReview.net."},{"key":"e_1_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/3653070"},{"key":"e_1_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1080\/13658816.2021.2004602"},{"key":"e_1_2_1_41_1","unstructured":"Rohin Manvi Samar Khanna Gengchen Mai Marshall Burke David B. Lobell and Stefano Ermon. 2024. GeoLLM: Extracting Geospatial Knowledge from Large Language Models. In ICLR. OpenReview.net."},{"key":"e_1_2_1_42_1","unstructured":"Yongli Mou Li Liu Sulayman K. Sowe Diego Collarana and Stefan Decker. 2024. Leveraging LLMs Few-shot Learning to Improve Instruction-driven Knowledge Graph Construction. In VLDB. VLDB.org."},{"key":"e_1_2_1_43_1","volume-title":"M. Saquib Sarfraz, and Mohsen Ali.","author":"Munir Muhammad Akhtar","year":"2021","unstructured":"Muhammad Akhtar Munir, Muhammad Haris Khan, M. Saquib Sarfraz, and Mohsen Ali. 2021. SSAL: Synergizing between Self-Training and Adversarial Learning for Domain Adaptive Object Detection. In NIPS, Marc'Aurelio Ranzato, Alina Beygelzimer, Yann N. Dauphin, Percy Liang, and Jennifer Wortman Vaughan (Eds.). 22770-22782."},{"key":"e_1_2_1_44_1","unstructured":"Yansong Ning and Hao Liu. 2024. UrbanKGent: A Unified Large Language Model Agent Framework for Urban Knowledge Graph Construction. In NIPS Amir Globersons Lester Mackey Danielle Belgrave Angela Fan Ulrich Paquet Jakub M. Tomczak and Cheng Zhang (Eds.)."},{"key":"e_1_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2023.3283520"},{"key":"e_1_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3191696"},{"key":"e_1_2_1_47_1","unstructured":"Alec Radford Karthik Narasimhan Tim Salimans and Ilya Sutskever. 2018. Improving Language Understanding by Generative Pre-Training. In OpenAI Technical Report. OpenAI."},{"key":"e_1_2_1_48_1","first-page":"1","article-title":"Cardinality Estimation over Knowledge Graphs with Embeddings and Graph Neural Networks","volume":"2","author":"Schwabe Tim","year":"2024","unstructured":"Tim Schwabe and Maribel Acosta. 2024. Cardinality Estimation over Knowledge Graphs with Embeddings and Graph Neural Networks. In SIGMOD, Vol. 2. 44:1-44:26.","journal-title":"SIGMOD"},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2025.3527978"},{"key":"e_1_2_1_50_1","volume-title":"Encyclopedia of GIS","author":"Strobl Christian","unstructured":"Christian Strobl. 2017. Dimensionally Extended Nine-Intersection Model (DE-9IM). In Encyclopedia of GIS, Shashi Shekhar, Hui Xiong, and Xun Zhou (Eds.). Springer, 470-476."},{"key":"e_1_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/3530811"},{"key":"e_1_2_1_52_1","unstructured":"Hugo Touvron Louis Martin Kevin Stone Peter Albert Amjad Almahairi Yasmine Babaei Nikolay Bashlykov Soumya Batra Prajjwal Bhargava Shruti Bhosale Dan Bikel Lukas Blecher Cristian Canton-Ferrer Moya Chen Guillem Cucurull David Esiobu Jude Fernandes Jeremy Fu Wenyin Fu Brian Fuller Cynthia Gao Vedanuj Goswami Naman Goyal Anthony Hartshorn Saghar Hosseini Rui Hou Hakan Inan Marcin Kardas Viktor Kerkez Madian Khabsa Isabel Kloumann Artem Korenev Punit Singh Koura Marie-Anne Lachaux Thibaut Lavril Jenya Lee Diana Liskovich Yinghai Lu Yuning Mao Xavier Martinet Todor Mihaylov Pushkar Mishra Igor Molybog Yixin Nie Andrew Poulton Jeremy Reizenstein Rashi Rungta Kalyan Saladi Alan Schelten Ruan Silva Eric Michael Smith Ranjan Subramanian Xiaoqing Ellen Tan Binh Tang Ross Taylor Adina Williams Jian Xiang Kuan Puxin Xu Zheng Yan Iliyan Zarov Yuchen Zhang Angela Fan Melanie Kambadur Sharan Narang Aur\u00e9lien Rodriguez Robert Stojnic Sergey Edunov and Thomas Scialom. 2023. Llama 2: Open Foundation and Fine-Tuned Chat Models. In CoRR Vol. abs\/2307.09288."},{"key":"e_1_2_1_53_1","volume-title":"Brian Lester, Nan Du, Andrew M. Dai, and Quoc V. Le.","author":"Wei Jason","year":"2022","unstructured":"Jason Wei, Maarten Bosma, Vincent Y. Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M. Dai, and Quoc V. Le. 2022. Finetuned Language Models are Zero-Shot Learners. In ICLR. OpenReview.net."},{"key":"e_1_2_1_54_1","volume-title":"CoRR","author":"Yang Dongchao","unstructured":"Dongchao Yang, Jinchuan Tian, Xu Tan, Rongjie Huang, Songxiang Liu, Xuankai Chang, Jiatong Shi, Sheng Zhao, Jiang Bian, Xixin Wu, Zhou Zhao, Shinji Watanabe, and Helen Meng. 2023. UniAudio: An Audio Foundation Model Toward Universal Audio Generation. In CoRR, Vol. abs\/2310.00704."},{"key":"e_1_2_1_55_1","first-page":"2873","article-title":"Weakly-supervised Temporal Path Representation Learning with Contrastive Curriculum Learning","author":"Yang Sean Bin","year":"2022","unstructured":"Sean Bin Yang, Chenjuan Guo, Jilin Hu, Bin Yang, Jian Tang, and Christian S. Jensen. 2022. Weakly-supervised Temporal Path Representation Learning with Contrastive Curriculum Learning. In ICDE. IEEE, 2873-2885.","journal-title":"ICDE. IEEE"},{"key":"e_1_2_1_56_1","volume-title":"CoRR","author":"Yao Liang","unstructured":"Liang Yao, Chengsheng Mao, and Yuan Luo. 2019. KG-BERT: BERT for Knowledge Graph Completion. In CoRR, Vol. abs\/1909.03193."},{"key":"e_1_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP49660.2025.10889242"},{"key":"e_1_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.14778\/3654621.3654628"},{"key":"e_1_2_1_59_1","first-page":"1262","article-title":"Distributed In-memory Trajectory Similarity Search and Join on Road Network","author":"Yuan Haitao","year":"2019","unstructured":"Haitao Yuan and Guoliang Li. 2019. Distributed In-memory Trajectory Similarity Search and Join on Road Network. In ICDE. IEEE, 1262-1273.","journal-title":"ICDE. IEEE"},{"key":"e_1_2_1_60_1","first-page":"348","article-title":"An Effective Joint Prediction Model for Travel Demands and Traffic Flows","author":"Yuan Haitao","year":"2021","unstructured":"Haitao Yuan, Guoliang Li, Zhifeng Bao, and Ling Feng. 2021. An Effective Joint Prediction Model for Travel Demands and Traffic Flows. In ICDE. IEEE, 348-359.","journal-title":"ICDE. IEEE"},{"key":"e_1_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.14778\/3611479.3611504"},{"key":"e_1_2_1_62_1","first-page":"103808","article-title":"BB-GeoGPT: A framework for learning a large language model for geographic information science","volume":"61","author":"Zhang Yifan","year":"2024","unstructured":"Yifan Zhang, Zhiyun Wang, Zhengting He, Jingxuan Li, Gengchen Mai, Jianfeng Lin, Cheng Wei, and Wenhao Yu. 2024. BB-GeoGPT: A framework for learning a large language model for geographic information science. In IPM, Vol. 61. 103808.","journal-title":"IPM"},{"key":"e_1_2_1_63_1","first-page":"103976","article-title":"GeoGPT: An assistant for understanding and processing geospatial tasks","volume":"131","author":"Zhang Yifan","year":"2024","unstructured":"Yifan Zhang, ChengWei, Zhengting He, andWenhao Yu. 2024. GeoGPT: An assistant for understanding and processing geospatial tasks. In AEOG, Vol. 131. 103976.","journal-title":"AEOG"},{"key":"e_1_2_1_64_1","volume-title":"CoRR","author":"Zhao Wayne Xin","year":"1822","unstructured":"Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, Yifan Du, Chen Yang, Yushuo Chen, Zhipeng Chen, Jinhao Jiang, Ruiyang Ren, Yifan Li, Xinyu Tang, Zikang Liu, Peiyu Liu, Jian-Yun Nie, and Ji-Rong Wen. 2023. A Survey of Large Language Models. In CoRR, Vol. abs\/2303.18223."},{"key":"e_1_2_1_65_1","first-page":"1","article-title":"RLER-TTE: An Efficient and Effective Framework for En Route Travel Time Estimation with Reinforcement Learning","volume":"3","author":"Zheng Zhihan","year":"2025","unstructured":"Zhihan Zheng, Haitao Yuan, Minxiao Chen, and Shangguang Wang. 2025. RLER-TTE: An Efficient and Effective Framework for En Route Travel Time Estimation with Reinforcement Learning. In SIGMOD, Vol. 3. 71:1-71:26.","journal-title":"SIGMOD"},{"key":"e_1_2_1_66_1","unstructured":"Deyao Zhu Jun Chen Xiaoqian Shen Xiang Li and Mohamed Elhoseiny. 2024. MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models. In ICLR. OpenReview.net."},{"key":"e_1_2_1_67_1","first-page":"5429","article-title":"Chat2Query","author":"Zhu Jun-Peng","year":"2024","unstructured":"Jun-Peng Zhu, Peng Cai, Boyan Niu, Zheming Ni, Kai Xu, Jiajun Huang, Jianwei Wan, Shengbo Ma, Bing Wang, Donghui Zhang, Liu Tang, and Qi Liu. 2024. Chat2Query: A Zero-Shot Automatic Exploratory Data Analysis System with Large Language Models. In ICDE. IEEE, 5429-5432.","journal-title":"In ICDE. IEEE"}],"container-title":["Proceedings of the ACM on Management of Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3769796","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T04:28:43Z","timestamp":1775536123000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3769796"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,4]]},"references-count":67,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,12,4]]}},"alternative-id":["10.1145\/3769796"],"URL":"https:\/\/doi.org\/10.1145\/3769796","relation":{},"ISSN":["2836-6573"],"issn-type":[{"value":"2836-6573","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,4]]}}}