{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T16:24:10Z","timestamp":1783700650772,"version":"3.55.0"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,1,8]],"date-time":"2025-01-08T00:00:00Z","timestamp":1736294400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,8]],"date-time":"2025-01-08T00:00:00Z","timestamp":1736294400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci. China Inf. Sci."],"published-print":{"date-parts":[[2025,2]]},"DOI":"10.1007\/s11432-023-4073-y","type":"journal-article","created":{"date-parts":[[2025,1,15]],"date-time":"2025-01-15T19:26:56Z","timestamp":1736969216000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["RE-SEGNN: recurrent semantic evidence-aware graph neural network for temporal knowledge graph forecasting"],"prefix":"10.1007","volume":"68","author":[{"given":"Wenyu","family":"Cai","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengfan","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuanhua","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanxin","family":"Fan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Quntao","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hai","family":"Jin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,1,8]]},"reference":[{"key":"4073_CR1","first-page":"6669","volume-title":"Proceedings of the Conference on Empirical Methods in Natural Language Processing","author":"W Jin","year":"2020","unstructured":"Jin W, Qu M, Jin X, et al. Recurrent event network: autoregressive structure inferenceover temporal knowledge graphs. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2020. 6669\u20136683"},{"key":"4073_CR2","doi-asserted-by":"publisher","first-page":"408","DOI":"10.1145\/3404835.3462963","volume-title":"Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Z Li","year":"2021","unstructured":"Li Z, Jin X, Li W, et al. Temporal knowledge graph reasoning based on evolutional representation learning. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2021. 408\u2013417"},{"key":"4073_CR3","first-page":"279","volume-title":"Proceedings of the International Conference on Data Mining","author":"K Liu","year":"2022","unstructured":"Liu K, Zhao F, Xu G, et al. Temporal knowledge graph reasoning via time-distributed representation Learning. In: Proceedings of the International Conference on Data Mining, Orlando, 2022. 279\u2013288"},{"key":"4073_CR4","first-page":"4732","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"C Zhu","year":"2021","unstructured":"Zhu C, Chen M, Fan C, et al. Learning from history: modeling temporal knowledge graphs with sequential copy-generation networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, 2021. 4732\u20134740"},{"key":"4073_CR5","first-page":"5730","volume-title":"Proceedings of the Conference on Empirical Methods in Natural Language Processing","author":"J Wu","year":"2020","unstructured":"Wu J, Cao M, Cheung J C K, et al. TeMP: temporal message passing for temporal knowledge graph completion. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2020. 5730\u20135746"},{"key":"4073_CR6","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1093\/biomet\/58.1.83","volume":"58","author":"A G Hawkes","year":"1971","unstructured":"Hawkes A G. Spectra of some self-exciting and mutually exciting point processes. Biometrika, 1971, 58: 83\u201390","journal-title":"Biometrika"},{"key":"4073_CR7","first-page":"2787","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","author":"A Bordes","year":"2013","unstructured":"Bordes A, Usunier N, Garcia-Duran A, et al. Translating embeddings for modeling multi-relational data. In: Proceedings of the Advances in Neural Information Processing Systems, 2013. 2787\u20132795"},{"key":"4073_CR8","first-page":"1112","volume-title":"Proceedings of the 28th AAAI Conference on Artificial Intelligence","author":"Z Wang","year":"2014","unstructured":"Wang Z, Zhang J, Feng J, et al. Knowledge graph embedding by translating on hyperplanes. In: Proceedings of the 28th AAAI Conference on Artificial Intelligence, Qu\u00e9bec, 2014. 1112\u20131119"},{"key":"4073_CR9","first-page":"809","volume-title":"Proceedings of the 28th International Conference on Machine Learning","author":"M Nickel","year":"2011","unstructured":"Nickel M, Tresp V, Kriegel H P. A three-way model for collective learning on multi-relational data. In: Proceedings of the 28th International Conference on Machine Learning, Washington, 2011. 809\u2013816"},{"key":"4073_CR10","volume-title":"Proceedings of the International Conference on Learning Representations","author":"B Yang","year":"2015","unstructured":"Yang B, Yih W, He X, et al. Embedding entities and relations for learning and inference in knowledge bases. In: Proceedings of the International Conference on Learning Representations, San Diego, 2015"},{"key":"4073_CR11","first-page":"2071","volume-title":"Proceedings of the International Conference on Machine Learning","author":"T Trouillon","year":"2016","unstructured":"Trouillon T, Welbl J, Riedel S, et al. Complex embeddings for simple link prediction. In: Proceedings of the International Conference on Machine Learning, New York City, 2016. 2071\u20132080"},{"key":"4073_CR12","first-page":"1811","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"T Dettmers","year":"2018","unstructured":"Dettmers T, Minervini P, Stenetorp P, et al. Convolutional 2D knowledge graph embeddings. In: Proceedings of the AAAI Conference on Artificial Intelligence, Shenzhen, 2018. 1811\u20131818"},{"key":"4073_CR13","first-page":"2180","volume-title":"Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","author":"T Vu","year":"2019","unstructured":"Vu T, Nguyen T D, Nguyen D Q, et al. A capsule network-based embedding model for knowledge graph completion and search personalization. In: Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Minneapolis, 2019. 2180\u20132189"},{"key":"4073_CR14","first-page":"327","volume-title":"Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","author":"D Q Nguyen","year":"2018","unstructured":"Nguyen D Q, Nguyen T D, Nguyen D Q, et al. A novel embedding model for knowledge base completion based on convolutional neural network. In: Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, New Orleans, 2018. 327\u2013333"},{"key":"4073_CR15","first-page":"564","volume-title":"Proceedings of the Conference on Empirical Methods in Natural Language Processing","author":"W Xiong","year":"2017","unstructured":"Xiong W, Hoang T, Wang W Y. DeepPath: a reinforcement learning method for knowledge graph reasoning. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, Copenhagen, 2017. 564\u2013573"},{"key":"4073_CR16","unstructured":"Yao L, Mao C, Luo Y. KG-BERT: bert for knowledge graph completion. 2019. ArXiv:1909.03193"},{"key":"4073_CR17","first-page":"4171","volume-title":"Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","author":"J Devlin","year":"2019","unstructured":"Devlin J, Chang M W, Lee K, et al. BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Minneapolis, 2019. 4171\u20134186"},{"key":"4073_CR18","first-page":"4281","volume-title":"Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics","author":"L Wang","year":"2022","unstructured":"Wang L, Zhao W, Wei Z, et al. SimKGC: simple contrastive knowledge graph completion with pre-trained language models. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, Dublin, 2022. 4281\u20134294"},{"key":"4073_CR19","first-page":"1965","volume-title":"Proceedings of the 29th International Conference on Computational Linguistics","author":"J Shen","year":"2022","unstructured":"Shen J, Wang C, Gong L, et al. Joint language semantic and structure embedding for knowledge graph completion. In: Proceedings of the 29th International Conference on Computational Linguistics, Gyeongju, 2022. 1965\u20131978"},{"key":"4073_CR20","first-page":"3078","volume-title":"Proceedings of the 31st International Joint Conference on Artificial Intelligence","author":"Z Hu","year":"2022","unstructured":"Hu Z, Guti\u00e9rrez-Basulto V, Xiang Z, et al. Type-aware embeddings for multi-hop reasoning over knowledge graphs. In: Proceedings of the 31st International Joint Conference on Artificial Intelligence, Vienna, 2022. 3078\u20133084"},{"key":"4073_CR21","first-page":"1172","volume-title":"Proceedings of the Findings of the Association for Computational Linguistics","author":"G Niu","year":"2020","unstructured":"Niu G, Li B, Zhang Y, et al. AutoETER: automated entity type representation for knowledge graph embedding. In: Proceedings of the Findings of the Association for Computational Linguistics, 2020. 1172\u20131181"},{"key":"4073_CR22","first-page":"2350","volume-title":"Proceedings of the Conference on Empirical Methods in Natural Language Processing","author":"T Jiang","year":"2016","unstructured":"Jiang T, Liu T, Ge T, et al. Encoding temporal information for time-aware link prediction. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, Austin, 2016. 2350\u20132354"},{"key":"4073_CR23","first-page":"2001","volume-title":"Proceedings of the Conference on Empirical Methods in Natural Language Processing","author":"S S Dasgupta","year":"2018","unstructured":"Dasgupta S S, Ray S N, Talukdar P P. HyTE: hyperplane-based temporally aware knowledge graph embedding. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, Brussels, 2018. 2001\u20132011"},{"key":"4073_CR24","first-page":"4816","volume-title":"Proceedings of the Conference on Empirical Methods in Natural Language Processing","author":"A Garc\u00eda-Dur\u00e1n","year":"2018","unstructured":"Garc\u00eda-Dur\u00e1n A, Duman\u010di\u0107 S, Niepert M. Learning sequence encoders for temporal knowledge graph completion. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, Brussels, 2018. 4816\u20134821"},{"key":"4073_CR25","volume-title":"Proceedings of the International Conference on Learning Representations","author":"T Lacroix","year":"2020","unstructured":"Lacroix T, Obozinski G, Usunier N. Tensor decompositions for temporal knowledge base completion. In: Proceedings of the International Conference on Learning Representations, Addis Ababa, 2020"},{"key":"4073_CR26","first-page":"3462","volume-title":"Proceedings of the 34th International Conference on Machine Learning","author":"R Trivedi","year":"2017","unstructured":"Trivedi R, Dai H, Wang Y, et al. Know-Evolve: deep temporal reasoning for dynamic knowledge graphs. In: Proceedings of the 34th International Conference on Machine Learning, Sydney, 2017. 3462\u20133471"},{"key":"4073_CR27","first-page":"8306","volume-title":"Proceedings of the Conference on Empirical Methods in Natural Language Processing","author":"H Sun","year":"2021","unstructured":"Sun H, Zhong J, Ma Y, et al. TimeTraveler: reinforcement learning for temporal knowledge graph forecasting. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, Punta Cana, 2021. 8306\u20138319"},{"key":"4073_CR28","first-page":"4732","volume-title":"Proceedings of the Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing","author":"Z Li","year":"2021","unstructured":"Li Z, Jin X, Guan S, et al. Search from history and reason for future: two-stage reasoning on temporal knowledge graphs. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, 2021. 4732\u20134743"},{"key":"4073_CR29","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Z Han","year":"2021","unstructured":"Han Z, Chen P, Ma Y, et al. Explainable subgraph reasoning for forecasting on temporal knowledge graphs. In: Proceedings of the International Conference on Learning Representations, 2021"},{"key":"4073_CR30","first-page":"2044","volume-title":"Proceedings of the 31st International Joint Conference on Artificial Intelligence","author":"Y Gao","year":"2022","unstructured":"Gao Y, Feng L, Kan Z, et al. Modeling precursors for temporal knowledge graph reasoning via auto-encoder structure. In: Proceedings of the 31st International Joint Conference on Artificial Intelligence, 2022. 2044\u20132051"},{"key":"4073_CR31","first-page":"2152","volume-title":"Proceedings of the 31st International Joint Conference on Artificial Intelligence","author":"Y Li","year":"2022","unstructured":"Li Y, Sun S, Zhao J. TiRGN: time-guided recurrent graph network with local-global historical patterns for temporal knowledge graph reasoning. In: Proceedings of the 31st International Joint Conference on Artificial Intelligence, Vienna, 2022. 2152\u20132158"},{"key":"4073_CR32","first-page":"1761","volume-title":"Proceedings of the 39th IEEE International Conference on Data Engineering","author":"K Liu","year":"2023","unstructured":"Liu K, Zhao F, Xu G, et al. RETIA: relation-entity twin-interact aggregation for temporal knowledge graph extrapolation. In: Proceedings of the 39th IEEE International Conference on Data Engineering, Anaheim, 2023. 1761\u20131774"},{"key":"4073_CR33","first-page":"1281","volume-title":"Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics","author":"Q Lin","year":"2023","unstructured":"Lin Q, Liu J, Mao R, et al. TECHS: temporal logical graph networks for explainable extrapolation reasoning. In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics, Toronto, 2023. 1281\u20131293"},{"key":"4073_CR34","doi-asserted-by":"publisher","first-page":"1559","DOI":"10.1145\/3539618.3591711","volume-title":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"K Liang","year":"2023","unstructured":"Liang K, Meng L, Liu M, et al. Learn from relational correlations and periodic events for temporal knowledge graph reasoning. In: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, Taipei, 2023. 1559\u20131568"},{"key":"4073_CR35","first-page":"7481","volume-title":"Proceedings of the Conference on Empirical Methods in Natural Language Processing","author":"H Sun","year":"2022","unstructured":"Sun H, Geng S, Zhong J, et al. Graph Hawkes transformer for extrapolated reasoning on temporal knowledge graphs. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, Abu Dhabi, 2022. 7481\u20137493"},{"key":"4073_CR36","first-page":"641","volume-title":"Proceedings of the International Conference on Artificial Intelligence and Statistics","author":"K Zhou","year":"2013","unstructured":"Zhou K, Zha H, Song L. Learning social infectivity in sparse low-rank networks using multi-dimensional Hawkes processes. In: Proceedings of the International Conference on Artificial Intelligence and Statistics, Atlanta, 2013. 641\u2013649"},{"key":"4073_CR37","first-page":"11183","volume-title":"Proceedings of the 37th International Conference on Machine Learning","author":"Q Zhang","year":"2020","unstructured":"Zhang Q, Lipani A, Kirnap O, et al. Self-attentive Hawkes process. In: Proceedings of the 37th International Conference on Machine Learning, 2020. 11183\u201311193"},{"key":"4073_CR38","first-page":"11692","volume-title":"Proceedings of the 37th International Conference on Machine Learning","author":"S Zuo","year":"2020","unstructured":"Zuo S, Jiang H, Li Z, et al. Transformer Hawkes process. In: Proceedings of the 37th International Conference on Machine Learning, 2020. 11692\u201311702"},{"key":"4073_CR39","first-page":"3060","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"C Shang","year":"2019","unstructured":"Shang C, Tang Y, Huang J, et al. End-to-end structure-aware convolutional networks for knowledge base completion. In: Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, 2019. 3060\u20133067"},{"key":"4073_CR40","first-page":"5781","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"R Li","year":"2022","unstructured":"Li R, Cao Y, Zhu Q, et al. How does knowledge graph embedding extrapolate to unseen data: a semantic evidence view. In: Proceedings of the AAAI Conference on Artificial Intelligence, 2022. 5781\u20135791"},{"key":"4073_CR41","first-page":"9266","volume-title":"Proceedings of International Conference on Computer Vision","author":"G Li","year":"2019","unstructured":"Li G, Muller M, Thabet A, et al. DeepGCNs: can GCNs go as deep as CNNs? In: Proceedings of International Conference on Computer Vision, Seoul, 2019. 9266\u20139275"},{"key":"4073_CR42","first-page":"1","volume-title":"Proceedings of the International Studies Association Annual Convention","author":"K Leetaru","year":"2013","unstructured":"Leetaru K, Schrodt P A. GDELT: global data on events, location, and tone, 1979\u20132012. In: Proceedings of the International Studies Association Annual Convention, San Francisco, 2013. 1\u201349"},{"key":"4073_CR43","first-page":"1771","volume-title":"Proceedings of the Web Conference","author":"J Leblay","year":"2018","unstructured":"Leblay J, Chekol M W. Deriving validity time in knowledge graph. In: Proceedings of the Web Conference, Lyon, 2018. 1771\u20131776"},{"key":"4073_CR44","volume-title":"Proceedings of the 7th Biennial Conference on Innovative Data Systems Research","author":"F Mahdisoltani","year":"2015","unstructured":"Mahdisoltani F, Biega J, Suchanek F. YAGO3: a knowledge base from multilingual Wikipedias. In: Proceedings of the 7th Biennial Conference on Innovative Data Systems Research, Asilomar, 2015"},{"key":"4073_CR45","first-page":"2","volume":"12","author":"E Boschee","year":"2015","unstructured":"Boschee E, Lautenschlager J, O\u2019Brien S, et al. ICEWS coded event data. Harvard Dataverse, 2015, 12: 2","journal-title":"Harvard Dataverse"},{"key":"4073_CR46","first-page":"3988","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"R Goel","year":"2020","unstructured":"Goel R, Kazemi S M, Brubaker M, et al. Diachronic embedding for temporal knowledge graph completion. In: Proceedings of the AAAI Conference on Artificial Intelligence, New York, 2020. 3988\u20133995"},{"key":"4073_CR47","first-page":"8352","volume-title":"Proceedings of the Conference on Empirical Methods in Natural Language Processing","author":"Z Han","year":"2021","unstructured":"Han Z, Ding Z, Ma Y, et al. Learning neural ordinary equations for forecasting future links on temporal knowledge graphs. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, Punta Cana, 2021. 8352\u20138364"},{"key":"4073_CR48","volume-title":"Proceedings of the Conference on Automated Knowledge Base Construction","author":"Z Han","year":"2020","unstructured":"Han Z, Ma Y, Wang Y, et al. Graph Hawkes neural network for forecasting on temporal knowledge graphs. In: Proceedings of the Conference on Automated Knowledge Base Construction, 2020"},{"key":"4073_CR49","doi-asserted-by":"publisher","first-page":"112103","DOI":"10.1007\/s11432-020-3182-1","volume":"65","author":"H L Dai","year":"2022","unstructured":"Dai H L, Peng X, Shi X H, et al. Reveal training performance mystery between TensorFlow and PyTorch in the single GPU environment. Sci China Inf Sci, 2022, 65: 112103","journal-title":"Sci China Inf Sci"}],"container-title":["Science China Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11432-023-4073-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11432-023-4073-y","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11432-023-4073-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,22]],"date-time":"2026-03-22T22:02:24Z","timestamp":1774216944000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11432-023-4073-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,8]]},"references-count":49,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,2]]}},"alternative-id":["4073"],"URL":"https:\/\/doi.org\/10.1007\/s11432-023-4073-y","relation":{},"ISSN":["1674-733X","1869-1919"],"issn-type":[{"value":"1674-733X","type":"print"},{"value":"1869-1919","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,8]]},"assertion":[{"value":"1 July 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 November 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 March 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 January 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"122104"}}