{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T12:54:00Z","timestamp":1774011240333,"version":"3.50.1"},"reference-count":39,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100021856","name":"Ministero dell'Universit\u00e0 e della Ricerca","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100021856","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000780","name":"European Commission","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neurocomputing"],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1016\/j.neucom.2026.132846","type":"journal-article","created":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T15:56:51Z","timestamp":1769702211000},"page":"132846","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Tensor factorization for temporal knowledge graph forecasting"],"prefix":"10.1016","volume":"674","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4861-455X","authenticated-orcid":false,"given":"Manuel","family":"Dileo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8442-602X","authenticated-orcid":false,"given":"Pasquale","family":"Minervini","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Matteo","family":"Zignani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3779-2809","authenticated-orcid":false,"given":"Sabrina","family":"Gaito","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"issue":"4","key":"10.1016\/j.neucom.2026.132846_bib0005","doi-asserted-by":"crossref","first-page":"71:1","DOI":"10.1145\/3447772","article-title":"Knowledge graphs","volume":"54","author":"Hogan","year":"2022","journal-title":"ACM Comput. Surv."},{"issue":"13","key":"10.1016\/j.neucom.2026.132846_bib0010","doi-asserted-by":"crossref","first-page":"i457","DOI":"10.1093\/bioinformatics\/bty294","article-title":"Modeling polypharmacy side effects with graph convolutional networks","volume":"34","author":"Zitnik","year":"2018","journal-title":"Bioinformation"},{"key":"10.1016\/j.neucom.2026.132846_bib0015","author":"Gema"},{"issue":"2","key":"10.1016\/j.neucom.2026.132846_bib0020","doi-asserted-by":"crossref","first-page":"1679","DOI":"10.1093\/bib\/bbaa012","article-title":"Biological applications of knowledge graph embedding models","volume":"22","author":"Mohamed","year":"2021","journal-title":"Brief. Bioinform."},{"issue":"8","key":"10.1016\/j.neucom.2026.132846_bib0025","doi-asserted-by":"crossref","first-page":"3549","DOI":"10.1109\/TKDE.2020.3028705","article-title":"A survey on knowledge graph-based recommender systems","volume":"34","author":"Guo","year":"2022","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.neucom.2026.132846_bib0030","series-title":"HLT","article-title":"WORDNET: a lexical database for English","author":"Miller","year":"1992"},{"issue":"8","key":"10.1016\/j.neucom.2026.132846_bib0035","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1145\/3331166","article-title":"Industry-scale knowledge graphs: lessons and challenges","volume":"62","author":"Noy","year":"2019","journal-title":"Commun. ACM"},{"key":"10.1016\/j.neucom.2026.132846_bib0040","series-title":"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics","article-title":"Learning sequence encoders for temporal knowledge graph completion","author":"Garc\u00eda-Dur\u00e1n","year":"2020"},{"key":"10.1016\/j.neucom.2026.132846_bib0045","series-title":"Machine Learning and Knowledge Discovery in Databases: Research Track","first-page":"533","article-title":"Comparing apples and oranges? On the evaluation of methods for temporal knowledge graph forecasting","author":"Gastinger","year":"2023"},{"key":"10.1016\/j.neucom.2026.132846_bib0050","series-title":"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)","first-page":"6669","article-title":"Recurrent event network: autoregressive structure inferenceover temporal knowledge graphs","author":"Jin","year":"2020"},{"key":"10.1016\/j.neucom.2026.132846_bib0055","series-title":"Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval;","article-title":"Temporal knowledge graph reasoning based on evolutional representation learning","author":"Li","year":"2021"},{"key":"10.1016\/j.neucom.2026.132846_bib0060","series-title":"Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2025, Padua, Italy, July 13\u201318, 2025","first-page":"2550","article-title":"Htgformer: heterogeneous temporal graph transformer","author":"Wang","year":"2025"},{"key":"10.1016\/j.neucom.2026.132846_bib0065","series-title":"ICLR","article-title":"You CAN teach an old dog new tricks! On training knowledge graph embeddings","author":"Ruffinelli","year":"2020"},{"key":"10.1016\/j.neucom.2026.132846_bib0070","series-title":"Proceedings of the International Conference on Machine Learning (ICML)","first-page":"2071","article-title":"Complex embeddings for simple link prediction","author":"Trouillon","year":"2016"},{"key":"10.1016\/j.neucom.2026.132846_bib0075","series-title":"ICLR","article-title":"Tensor decompositions for temporal knowledge base completion","author":"Lacroix","year":"2020"},{"key":"10.1016\/j.neucom.2026.132846_bib0080","series-title":"Advances in Neural Information Processing Systems","first-page":"77713","article-title":"How to turn your knowledge graph embeddings into generative models","volume":"vol. 36","author":"Loconte","year":"2023"},{"key":"10.1016\/j.neucom.2026.132846_bib0085","series-title":"9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021","article-title":"Complex query answering with neural link predictors","author":"Arakelyan","year":"2021"},{"key":"10.1016\/j.neucom.2026.132846_bib0090","series-title":"ICLR","article-title":"Tensor decompositions for temporal knowledge base completion","author":"Lacroix","year":"2020"},{"key":"10.1016\/j.neucom.2026.132846_bib0095","series-title":"ESANN","article-title":"Enhancing neural link predictors for temporal knowledge graphs with temporal regularisers","author":"Dileo","year":"2025"},{"key":"10.1016\/j.neucom.2026.132846_bib0100","series-title":"ISA Annual ConventionIntroduced GDELT event database;","article-title":"Gdelt: global data on events, location and tone, 1979\u20132012","author":"Leetaru","year":"2013"},{"key":"10.1016\/j.neucom.2026.132846_bib0105","series-title":"EMNLP (1)","first-page":"8306","article-title":"Timetraveler: reinforcement learning for temporal knowledge graph forecasting","author":"Sun","year":"2021"},{"key":"10.1016\/j.neucom.2026.132846_bib0110","series-title":"AAAI","first-page":"4120","article-title":"Tlogic: temporal logical rules for explainable link forecasting on temporal knowledge graphs","author":"Liu","year":"2022"},{"key":"10.1016\/j.neucom.2026.132846_bib0115","series-title":"AAAI","first-page":"4732","article-title":"Learning from history: modeling temporal knowledge graphs with sequential copy-generation networks","author":"Zhu","year":"2021"},{"key":"10.1016\/j.neucom.2026.132846_bib0120","series-title":"Proceedings of the International Conference on Learning Representations (ICLR)","article-title":"Embedding entities and relations for learning and inference in knowledge bases","author":"Yang","year":"2015"},{"key":"10.1016\/j.neucom.2026.132846_bib0125","series-title":"Advances in Neural Information Processing Systems (NeurIPS)","first-page":"2787","article-title":"Translating embeddings for modeling multi-relational data","author":"Bordes","year":"2013"},{"key":"10.1016\/j.neucom.2026.132846_bib0130","series-title":"International Conference on Learning Representations (ICLR)","article-title":"Rotate: knowledge graph embedding by relational rotation in complex space","author":"Sun","year":"2019"},{"key":"10.1016\/j.neucom.2026.132846_bib0135","author":"Irani"},{"issue":"3","key":"10.1016\/j.neucom.2026.132846_bib0140","doi-asserted-by":"crossref","first-page":"239","DOI":"10.3103\/S0146411622030026","article-title":"Time series prediction using radial basis function network with transformation of training data and its applications","volume":"56","author":"Kitayama","year":"2022","journal-title":"Autom. Control. Comput. Sci."},{"key":"10.1016\/j.neucom.2026.132846_bib0145","author":"Kazemi"},{"key":"10.1016\/j.neucom.2026.132846_bib0150","series-title":"IJCAI","first-page":"4016","article-title":"History repeats itself: a baseline for temporal knowledge graph forecasting","author":"Gastinger","year":"2024"},{"key":"10.1016\/j.neucom.2026.132846_bib0155","series-title":"NAACL-HLT","first-page":"2569","article-title":"Temporal knowledge graph completion using a linear temporal regularizer and multivector embeddings","author":"Xu","year":"2021"},{"key":"10.1016\/j.neucom.2026.132846_bib0160","series-title":"Proceedings of the 34th International Conference on Machine Learning - Volume 70 ICML\u201917","first-page":"1263","article-title":"Neural message passing for quantum chemistry","author":"Gilmer","year":"2017"},{"key":"10.1016\/j.neucom.2026.132846_bib0165","series-title":"ICML, Vol. 80 of Proceedings of Machine Learning Research","first-page":"2869","article-title":"Canonical tensor decomposition for knowledge base completion","author":"Lacroix","year":"2018"},{"key":"10.1016\/j.neucom.2026.132846_bib0170","series-title":"ICLR","article-title":"Tensor decompositions for temporal knowledge base completion","author":"Lacroix","year":"2020"},{"key":"10.1016\/j.neucom.2026.132846_bib0175","series-title":"EMNLP (1)","first-page":"5730","article-title":"Temp: temporal message passing for temporal knowledge graph completion","author":"Wu","year":"2020"},{"key":"10.1016\/j.neucom.2026.132846_bib0180","series-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems (NeurIPS)","first-page":"6000","article-title":"Attention is all you need","author":"Vaswani","year":"2017"},{"key":"10.1016\/j.neucom.2026.132846_bib0185","series-title":"NeurIPS","article-title":"Refactor gnns: revisiting factorisation-based models from a message-passing perspective","author":"Chen","year":"2022"},{"key":"10.1016\/j.neucom.2026.132846_bib0190","article-title":"Gnnexplainer: generating explanations for graph neural networks","volume":"32","author":"Ying","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.neucom.2026.132846_bib0195","article-title":"Evaluating explainability techniques on discrete-time graph neural networks","author":"Dileo","year":"2025","journal-title":"Trans. Mach. Learn. Res."}],"container-title":["Neurocomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231226002432?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231226002432?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T10:43:13Z","timestamp":1774003393000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0925231226002432"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4]]},"references-count":39,"alternative-id":["S0925231226002432"],"URL":"https:\/\/doi.org\/10.1016\/j.neucom.2026.132846","relation":{},"ISSN":["0925-2312"],"issn-type":[{"value":"0925-2312","type":"print"}],"subject":[],"published":{"date-parts":[[2026,4]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Tensor factorization for temporal knowledge graph forecasting","name":"articletitle","label":"Article Title"},{"value":"Neurocomputing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neucom.2026.132846","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"132846"}}