{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T06:53:35Z","timestamp":1777013615366,"version":"3.51.4"},"reference-count":65,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T00:00:00Z","timestamp":1776902400000},"content-version":"vor","delay-in-days":112,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U2268204"],"award-info":[{"award-number":["U2268204"]}],"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":["62172061"],"award-info":[{"award-number":["62172061"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2023YFB3308300"],"award-info":[{"award-number":["2023YFB3308300"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012542","name":"Sichuan Province Science and Technology Support Program","doi-asserted-by":"publisher","award":["2024ZDZX0012"],"award-info":[{"award-number":["2024ZDZX0012"]}],"id":[{"id":"10.13039\/100012542","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012542","name":"Sichuan Province Science and Technology Support Program","doi-asserted-by":"publisher","award":["2023ZHCG0011"],"award-info":[{"award-number":["2023ZHCG0011"]}],"id":[{"id":"10.13039\/100012542","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012542","name":"Sichuan Province Science and Technology Support Program","doi-asserted-by":"publisher","award":["2021YFG0152"],"award-info":[{"award-number":["2021YFG0152"]}],"id":[{"id":"10.13039\/100012542","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Intelligent Systems"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:p>\n                    Temporal knowledge graph reasoning (TKGR) excels at inferring missing event\u2010centric facts within a timeline, thereby mitigating the inherent incompleteness of real\u2010world data. Existing TKGR methods predominantly exploit intrasnapshot structural patterns and intersnapshot temporal dependencies. However, they often fail to incorporate valuable time\u2010invariant factual knowledge about events and struggle to jointly model entity\u2013relation evolution dynamics. To address these challenges, we propose CKE\n                    <jats:sup>3<\/jats:sup>\n                    , a novel contrastive knowledge\u2010enhanced event evolution model that integrates time\u2010invariant entity background knowledge with time\u2010variant structural information for enhanced reasoning. Specifically, CKE\n                    <jats:sup>3<\/jats:sup>\n                    improves the learning of expressive entity and relation representations through an adaptive knowledge retrieval strategy, which constructs descriptive common sense knowledge associated with each entity to provide richer contextual information. Moreover, we introduce a knowledge\u2010aware hierarchical structural\u2013temporal modeling module that jointly captures entity\u2013relation structural interactions as well as sequential and periodic temporal patterns across knowledge graph snapshots. To further mitigate the impact of knowledge noise, we design a self\u2010supervised augmentation task that promotes the fusion of relevant information while alleviating noise. Extensive experiments on four public TKGR datasets demonstrate that CKE\n                    <jats:sup>3<\/jats:sup>\n                    consistently surpasses state\u2010of\u2010the\u2010art baselines, highlighting its effectiveness in accurate entity forecasting for the TKGR task.\n                  <\/jats:p>","DOI":"10.1155\/int\/4624944","type":"journal-article","created":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T06:26:01Z","timestamp":1777011961000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Unifying Invariant and Variant Knowledge With Dual\u2010Hypergraph Contrastive Evolution for Temporal Knowledge Graph Reasoning"],"prefix":"10.1155","volume":"2026","author":[{"given":"Fei","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0679-4601","authenticated-orcid":false,"given":"Bing","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhangtao","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Shen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingjie","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fan","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2026,4,23]]},"reference":[{"key":"e_1_2_11_1_2","article-title":"Unifying Large Language Models and Knowledge Graphs: A Roadmap","author":"Pan S.","year":"2024","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_2_11_2_2","doi-asserted-by":"publisher","DOI":"10.1155\/2024\/2907043"},{"key":"e_1_2_11_3_2","doi-asserted-by":"publisher","DOI":"10.1002\/int.22955"},{"key":"e_1_2_11_4_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33016244"},{"key":"e_1_2_11_5_2","first-page":"1753","volume-title":"The Association for Computational Linguistics","author":"Zeng D.","year":"2015"},{"key":"e_1_2_11_6_2","first-page":"334","volume-title":"ACM","author":"Cheng Z.","year":"2025"},{"key":"e_1_2_11_7_2","doi-asserted-by":"publisher","DOI":"10.1145\/3535101"},{"key":"e_1_2_11_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/tkde.2023.3272652"},{"key":"e_1_2_11_9_2","first-page":"408","volume-title":"ACM","author":"Li Z.","year":"2021"},{"key":"e_1_2_11_10_2","article-title":"GTRL: An Entity Group-Aware Temporal Knowledge Graph Representation Learning Method","author":"Tang X.","year":"2023","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_2_11_11_2","first-page":"5730","volume-title":"Association for Computational Linguistics","author":"Wu J.","year":"2020"},{"key":"e_1_2_11_12_2","article-title":"Temporal Knowledge Graph Reasoning With Dynamic Memory Enhancement","author":"Zhang F.","year":"2024","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_2_11_13_2","article-title":"Reasoning Over Different Types of Knowledge Graphs: Static, Temporal and Multi-Modal","author":"Liang K.","year":"2022","journal-title":"Arxiv Preprint Arxiv:2212.05767"},{"key":"e_1_2_11_14_2","first-page":"6669","volume-title":"Association for Computational Linguistics","author":"Jin W.","year":"2020"},{"key":"e_1_2_11_15_2","first-page":"4765","volume-title":"37. 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