{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T16:03:57Z","timestamp":1772121837697,"version":"3.50.1"},"reference-count":30,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T00:00:00Z","timestamp":1772064000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Overseas Postdoctoral Talent Recruitment Program of China and Tianjin University"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Temporal knowledge graphs aim to enhance the dynamic and evolutionary representation of knowledge while enabling time-based reasoning. However, the reasoning based on temporal knowledge graphs in real geographic environments suffers from low accuracy due to the difficulty in effectively utilizing complex spatio-temporal information. Spatial attributes within entities typically encompass both relative and absolute spatial information types. However, during spatio-temporal reasoning, the deep coupling between the quadruple (entities,\u00a0\u00a0relations,\u00a0\u00a0timestamp) and these two spatial information types is frequently overlooked, as they remain unintegrated in inference predictions. This paper proposes a novel Multi-Task Spatial Recurrent Evolution Graph Convolutional Network (MTS-RE-GCN) framework to enable temporal knowledge graph methods to better reason about spatial entities under time-varying conditions. Experiments on the spatio-temporal dataset and the benchmark dataset (i.e., ICEWS14s, ICEWS18) with spatio-temporal features demonstrate that MTS-RE-GCN significantly outperforms the baseline models (e.g., RE-GCN, TiRGN). For entity prediction tasks, MTS-RE-GCN achieves mean reciprocal rank (MRR) scores of 0.848, 0.739, 0.566, representing improvements of 9.00%, 6.03%, 3.28%, correspondingly. This provides a comprehensive and efficient solution for spatio-temporal entity prediction in temporal knowledge graphs, holding significant implications for spatio-temporal data analysis, event prediction, and related fields.<\/jats:p>","DOI":"10.3390\/ijgi15030097","type":"journal-article","created":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T14:51:29Z","timestamp":1772117489000},"page":"97","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MTS-RE-GCN: Multi-Task Methods for Enhanced Spatio-Temporal Reasoning in Temporal Knowledge Graphs"],"prefix":"10.3390","volume":"15","author":[{"given":"Yuhao","family":"Huo","sequence":"first","affiliation":[{"name":"School of Mechanics and Engineering Science, Peking University, Beijing 100871, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5028-2291","authenticated-orcid":false,"given":"Guangyuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Earth System Science, Tianjin University, Tianjin 300072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4890-7563","authenticated-orcid":false,"given":"Bing","family":"Han","sequence":"additional","affiliation":[{"name":"School of Mathematical Sciences, Peking University, Beijing 100871, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9754-1502","authenticated-orcid":false,"given":"Xiaochong","family":"Tong","sequence":"additional","affiliation":[{"name":"School of Geospatial Information, Information Engineering University, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chengqi","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Mechanics and Engineering Science, Peking University, Beijing 100871, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"13071","DOI":"10.1007\/s10462-023-10465-9","article-title":"Knowledge Graphs: Opportunities and Challenges","volume":"56","author":"Peng","year":"2023","journal-title":"Artif. 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