{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T16:05:52Z","timestamp":1781193952144,"version":"3.54.1"},"reference-count":58,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100012899","name":"Lanzhou University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100012899","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62473181"],"award-info":[{"award-number":["62473181"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems with Applications"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.eswa.2026.132209","type":"journal-article","created":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T17:20:01Z","timestamp":1774545601000},"page":"132209","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Modeling bidirectional modes of an event for temporal knowledge graph reasoning"],"prefix":"10.1016","volume":"320","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0843-8536","authenticated-orcid":false,"given":"Zepeng","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6226-4325","authenticated-orcid":false,"given":"Rikui","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shilei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8443-8779","authenticated-orcid":false,"given":"Zhenwen","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianghong","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.eswa.2026.132209_bib0001","first-page":"2787","article-title":"Translating embeddings for modeling multi-relational data","volume":"vol. 26","author":"Bordes","year":"2013","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.eswa.2026.132209_bib0002","series-title":"Introduction to Bessel functions","author":"Bowman","year":"2012"},{"key":"10.1016\/j.eswa.2026.132209_bib0003","series-title":"Findings of the association for computational linguistics: ACL 2024","first-page":"5766","article-title":"Predicting the unpredictable: Uncertainty-aware reasoning over temporal knowledge graphs via diffusion process","author":"Cai","year":"2024"},{"key":"10.1016\/j.eswa.2026.132209_bib0004","series-title":"Proceedings of the 62nd annual meeting of the association for computational linguistics (volume 1: Long papers)","first-page":"117","article-title":"A unified temporal knowledge graph reasoning model towards interpolation and extrapolation","author":"Chen","year":"2024"},{"key":"10.1016\/j.eswa.2026.132209_bib0005","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.126226","article-title":"Temporal knowledge graph extrapolation with subgraph information bottleneck","volume":"268","author":"Chen","year":"2025","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.132209_bib0006","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2025.107186","article-title":"A rule- and query-guided reinforcement learning for extrapolation reasoning in temporal knowledge graphs","volume":"185","author":"Chen","year":"2025","journal-title":"Neural Networks"},{"key":"10.1016\/j.eswa.2026.132209_bib0007","unstructured":"van den, O. A., Li, Y., & Vinyals, O. (2018). Representation learning with contrastive predictive coding. arXiv: 1807.03748."},{"key":"10.1016\/j.eswa.2026.132209_bib0008","series-title":"Proceedings of the AAAI conference on artificial intelligence","article-title":"Convolutional 2D knowledge graph embeddings","volume":"vol. 32","author":"Dettmers","year":"2018"},{"key":"10.1016\/j.eswa.2026.132209_bib0009","series-title":"2017 IEEE 60th international midwest symposium on circuits and systems (MWSCAS)","first-page":"1597","article-title":"Gate-variants of gated recurrent unit (GRU) neural networks","author":"Dey","year":"2017"},{"issue":"2","key":"10.1016\/j.eswa.2026.132209_bib0010","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1364\/OL.13.000079","article-title":"Comparison of Bessel and Gaussian beams","volume":"13","author":"Durnin","year":"1988","journal-title":"Optics Letters"},{"issue":"2","key":"10.1016\/j.eswa.2026.132209_bib0011","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1207\/s15516709cog1402_1","article-title":"Finding structure in time","volume":"14","author":"Elman","year":"1990","journal-title":"Cognitive Science"},{"key":"10.1016\/j.eswa.2026.132209_bib0012","series-title":"Findings of the association for computational linguistics: ACL 2024","first-page":"6719","article-title":"Two-stage generative question answering on temporal knowledge graph using large language models","author":"Gao","year":"2024"},{"key":"10.1016\/j.eswa.2026.132209_bib0013","doi-asserted-by":"crossref","unstructured":"Garc\u00eda-Dur\u00e1n, A., Duman\u010di\u0107, S., & Niepert, M. (2018). Learning sequence encoders for temporal knowledge graph completion. arXiv: 1809.03202.","DOI":"10.18653\/v1\/D18-1516"},{"issue":"5","key":"10.1016\/j.eswa.2026.132209_bib0014","doi-asserted-by":"crossref","first-page":"1214","DOI":"10.3390\/rs14051214","article-title":"Disaster prediction knowledge graph based on multi-source spatio-temporal information","volume":"14","author":"Ge","year":"2022","journal-title":"Remote Sensing"},{"key":"10.1016\/j.eswa.2026.132209_bib0015","series-title":"Proceedings of the AAAI conference on artificial intelligence","first-page":"3988","article-title":"Diachronic embedding for temporal knowledge graph completion","volume":"vol. 34","author":"Goel","year":"2020"},{"key":"10.1016\/j.eswa.2026.132209_bib0016","series-title":"International conference on learning representations","article-title":"Explainable subgraph reasoning for forecasting on temporal knowledge graphs","author":"Han","year":"2020"},{"key":"10.1016\/j.eswa.2026.132209_bib0017","series-title":"Proceedings of the 2021 conference on empirical methods in natural language processing","first-page":"8352","article-title":"Learning neural ordinary equations for forecasting future links on temporal knowledge graphs","author":"Han","year":"2021"},{"issue":"8","key":"10.1016\/j.eswa.2026.132209_bib0018","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Computation"},{"key":"10.1016\/j.eswa.2026.132209_bib0019","series-title":"Proceedings of the 62nd annual meeting of the association for computational linguistics (volume 1: Long papers)","first-page":"10783","article-title":"Confidence is not timeless: Modeling temporal validity for rule-based temporal knowledge graph forecasting","author":"Huang","year":"2024"},{"key":"10.1016\/j.eswa.2026.132209_bib0020","series-title":"Proceedings of the 2020 international conference on learning representations","article-title":"Tensor decompositions for temporal knowledge base completion","author":"Lacroix","year":"2020"},{"key":"10.1016\/j.eswa.2026.132209_bib0021","series-title":"Companion proceedings of the web conference 2018","first-page":"1771","article-title":"Deriving validity time in knowledge graph","author":"Leblay","year":"2018"},{"key":"10.1016\/j.eswa.2026.132209_bib0022","series-title":"Proceedings of the 2023 conference on empirical methods in natural language processing","first-page":"544","article-title":"Temporal knowledge graph forecasting without knowledge using in-context learning","author":"Lee","year":"2023"},{"key":"10.1016\/j.eswa.2026.132209_bib0023","first-page":"1","article-title":"GDELT: Global data on events, location, and tone","volume":"2","author":"Leetaru","year":"2013","journal-title":"ISA Annual Convention"},{"key":"10.1016\/j.eswa.2026.132209_bib0024","series-title":"Findings of the association for computational linguistics: EMNLP 2023","first-page":"7885","article-title":"TR-rules: Rule-based model for link forecasting on temporal knowledge graph considering temporal redundancy","author":"Li","year":"2023"},{"key":"10.1016\/j.eswa.2026.132209_bib0025","series-title":"International joint conference on artificial intelligence","first-page":"2152","article-title":"TiRGN: Time-guided recurrent graph network with local-global historical patterns for temporal knowledge graph reasoning","author":"Li","year":"2022"},{"key":"10.1016\/j.eswa.2026.132209_bib0026","series-title":"Proceedings of the 60th annual meeting of the association for computational linguistics (volume 2: Short papers)","first-page":"290","article-title":"Complex evolutional pattern learning for temporal knowledge graph reasoning","author":"Li","year":"2022"},{"key":"10.1016\/j.eswa.2026.132209_bib0027","series-title":"Findings of the association for computational linguistics: EMNLP 2022","first-page":"7328","article-title":"HiSMatch: Historical structure matching based temporal knowledge graph reasoning","author":"Li","year":"2022"},{"key":"10.1016\/j.eswa.2026.132209_bib0028","series-title":"Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval","first-page":"408","article-title":"Temporal knowledge graph reasoning based on evolutional representation learning","author":"Li","year":"2021"},{"key":"10.1016\/j.eswa.2026.132209_bib0029","series-title":"Proceedings of the 46th international ACM SIGIR conference on research and development in information retrieval","first-page":"1559","article-title":"Learn from relational correlations and periodic events for temporal knowledge graph reasoning","author":"Liang","year":"2023"},{"key":"10.1016\/j.eswa.2026.132209_bib0030","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.113404","article-title":"Erd-net: Modeling entity and relation dynamics for temporal knowledge graph reasoning","volume":"317","author":"Liao","year":"2025","journal-title":"Knowledge-Based Systems"},{"key":"10.1016\/j.eswa.2026.132209_bib0031","series-title":"Findings of the association for computational linguistics: NAACL 2024","first-page":"4303","article-title":"GenTKG: Generative forecasting on temporal knowledge graph with large language models","author":"Liao","year":"2024"},{"key":"10.1016\/j.eswa.2026.132209_bib0032","series-title":"Proceedings of the 14th ACM international conference on web search and data mining","first-page":"535","article-title":"Learning dynamic embeddings for temporal knowledge graphs","author":"Liao","year":"2021"},{"key":"10.1016\/j.eswa.2026.132209_bib0033","series-title":"2023 IEEE 39th international conference on data engineering (ICDE)","first-page":"1761","article-title":"RETIA: relation-entity twin-interact aggregation for temporal knowledge graph extrapolation","author":"Liu","year":"2023"},{"key":"10.1016\/j.eswa.2026.132209_bib0034","first-page":"4120","article-title":"TLogic: Temporal logical rules for explainable link forecasting on temporal knowledge graphs","author":"Liu","year":"2022"},{"issue":"6","key":"10.1016\/j.eswa.2026.132209_bib0035","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2024.103848","article-title":"TaReT: Temporal knowledge graph reasoning based on topology-aware dynamic relation graph and temporal fusion","volume":"61","author":"Ma","year":"2024","journal-title":"Information Processing & Management"},{"issue":"5","key":"10.1016\/j.eswa.2026.132209_bib0036","doi-asserted-by":"crossref","first-page":"3077","DOI":"10.1109\/TSC.2021.3075053","article-title":"Temporal knowledge graph embedding for effective service recommendation","volume":"15","author":"Mezni","year":"2021","journal-title":"IEEE Transactions on Services Computing"},{"key":"10.1016\/j.eswa.2026.132209_bib0037","series-title":"Proceedings of the 2024 conference on empirical methods in natural language processing","first-page":"5232","article-title":"Multi-granularity history and entity similarity learning for temporal knowledge graph reasoning","author":"Mingcong","year":"2024"},{"key":"10.1016\/j.eswa.2026.132209_bib0038","series-title":"The semantic web: 15th international conference","first-page":"593","article-title":"Modeling relational data with graph convolutional networks","author":"Schlichtkrull","year":"2018"},{"key":"10.1016\/j.eswa.2026.132209_bib0039","series-title":"Proceedings of the AAAI conference on artificial intelligence","first-page":"3060","article-title":"End-to-end structure-aware convolutional networks for knowledge base completion","volume":"vol. 33","author":"Shang","year":"2019"},{"key":"10.1016\/j.eswa.2026.132209_bib0040","series-title":"Proceedings of the 2023 conference on empirical methods in natural language processing","first-page":"4497","article-title":"NeuSTIP: A neuro-symbolic model for link and time prediction in temporal knowledge graphs","author":"Singh","year":"2023"},{"key":"10.1016\/j.eswa.2026.132209_bib0041","unstructured":"Sun, Z., Deng, Z.-H., Nie, J.-Y., & Tang, J. (2019). RotatE: Knowledge graph embedding by relational rotation in complex space. arXiv: 1902.10197."},{"key":"10.1016\/j.eswa.2026.132209_bib0042","series-title":"International conference on machine learning","first-page":"2071","article-title":"Complex embeddings for simple link prediction","author":"Trouillon","year":"2016"},{"key":"10.1016\/j.eswa.2026.132209_bib0043","first-page":"6000","article-title":"Attention is all you need","volume":"vol. 30","author":"Vaswani","year":"2017","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"16","key":"10.1016\/j.eswa.2026.132209_bib0044","doi-asserted-by":"crossref","first-page":"19285","DOI":"10.1007\/s10489-023-04481-z","article-title":"GLANet: Temporal knowledge graph completion based on global and local information-aware network","volume":"53","author":"Wang","year":"2023","journal-title":"Applied Intelligence"},{"key":"10.1016\/j.eswa.2026.132209_bib0045","series-title":"International conference on machine learning","first-page":"6861","article-title":"Simplifying graph convolutional networks","author":"Wu","year":"2019"},{"key":"10.1016\/j.eswa.2026.132209_bib0046","series-title":"Proceedings of the 62nd annual meeting of the association for computational linguistics (volume 1: Long papers)","first-page":"15984","article-title":"COKE: A cognitive knowledge graph for machine theory of mind","author":"Wu","year":"2024"},{"key":"10.1016\/j.eswa.2026.132209_bib0047","series-title":"Findings of the association for computational linguistics: ACL 2024","first-page":"16144","article-title":"Chain-of-history reasoning for temporal knowledge graph forecasting","author":"Xia","year":"2024"},{"key":"10.1016\/j.eswa.2026.132209_bib0048","series-title":"Proceedings of the AAAI conference on artificial intelligence","first-page":"4765","article-title":"Temporal knowledge graph reasoning with historical contrastive learning","volume":"vol. 37","author":"Xu","year":"2023"},{"key":"10.1016\/j.eswa.2026.132209_bib0049","unstructured":"Yang, B., Yih, W.-t., He, X., Gao, J., & Deng, L. (2014). Embedding entities and relations for learning and inference in knowledge bases. arXiv: 1412.6575."},{"key":"10.1016\/j.eswa.2026.132209_bib0050","series-title":"Proceedings of the 11th international joint conference on knowledge graphs","first-page":"110","article-title":"An urban traffic knowledge graph-driven spatial-temporal graph convolutional network for traffic flow prediction","author":"Yang","year":"2022"},{"key":"10.1016\/j.eswa.2026.132209_bib0051","series-title":"Proceedings of the asian conference on computer vision","article-title":"RE-Net: A relation embedded deep model for au occurrence and intensity estimation","author":"Yang","year":"2020"},{"key":"10.1016\/j.eswa.2026.132209_bib0052","series-title":"International conference on machine learning","first-page":"39913","article-title":"Temporal label smoothing for early event prediction","author":"Y\u00e8che","year":"2023"},{"key":"10.1016\/j.eswa.2026.132209_bib0053","series-title":"Proceedings of the 62nd annual meeting of the association for computational linguistics (volume 1: Long papers)","first-page":"11074","article-title":"Simple but effective compound geometric operations for temporal knowledge graph completion","author":"Ying","year":"2024"},{"key":"10.1016\/j.eswa.2026.132209_bib0054","series-title":"Findings of the association for computational linguistics: EMNLP 2024","first-page":"8761","article-title":"SALMON: A structure-aware language model with logicality and densification strategy for temporal knowledge graph reasoning","author":"Zhang","year":"2024"},{"key":"10.1016\/j.eswa.2026.132209_bib0055","series-title":"Proceedings of the 61st annual meeting of the association for computational linguistics (volume 1: Long papers)","first-page":"12617","article-title":"Learning latent relations for temporal knowledge graph reasoning","author":"Zhang","year":"2023"},{"key":"10.1016\/j.eswa.2026.132209_bib0056","series-title":"Findings of the association for computational linguistics: EMNLP 2024","first-page":"7675","article-title":"Modeling historical relevant and local frequency context for representation-based temporal knowledge graph forecasting","author":"Zhang","year":"2024"},{"key":"10.1016\/j.eswa.2026.132209_bib0057","series-title":"Findings of the empirical methods in natural language processing","first-page":"7675","article-title":"Modeling historical relevant and local frequency context for representation-based temporal knowledge graph forecasting","author":"Zhang","year":"2024"},{"key":"10.1016\/j.eswa.2026.132209_bib0058","series-title":"Proceedings of the AAAI conference on artificial intelligence","first-page":"4732","article-title":"Learning from history: Modeling temporal knowledge graphs with sequential copy-generation networks","volume":"vol. 35","author":"Zhu","year":"2021"}],"container-title":["Expert Systems with Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S095741742601122X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S095741742601122X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T15:53:43Z","timestamp":1781193223000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S095741742601122X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":58,"alternative-id":["S095741742601122X"],"URL":"https:\/\/doi.org\/10.1016\/j.eswa.2026.132209","relation":{},"ISSN":["0957-4174"],"issn-type":[{"value":"0957-4174","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Modeling bidirectional modes of an event for temporal knowledge graph reasoning","name":"articletitle","label":"Article Title"},{"value":"Expert Systems with Applications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.eswa.2026.132209","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"132209"}}