{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T07:20:02Z","timestamp":1740122402972,"version":"3.37.3"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2023,4,9]],"date-time":"2023-04-09T00:00:00Z","timestamp":1680998400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,4,9]],"date-time":"2023-04-09T00:00:00Z","timestamp":1680998400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61906030"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005047","name":"Natural Science Foundation of Liaoning Province","doi-asserted-by":"publisher","award":["2020-BS-063"],"id":[{"id":"10.13039\/501100005047","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science and Technology Project of Liaoning Province","award":["2021JH2\/10300064"]},{"name":"Youth Science and Technology Star Support Program of Dalian City","award":["2021RQ057"]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Data Min Knowl Disc"],"published-print":{"date-parts":[[2023,7]]},"DOI":"10.1007\/s10618-023-00935-7","type":"journal-article","created":{"date-parts":[[2023,4,9]],"date-time":"2023-04-09T07:02:46Z","timestamp":1681023766000},"page":"1591-1608","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["One-shot relational learning for extrapolation reasoning on temporal knowledge graphs"],"prefix":"10.1007","volume":"37","author":[{"given":"Ruixin","family":"Ma","sequence":"first","affiliation":[]},{"given":"Biao","family":"Mei","sequence":"additional","affiliation":[]},{"given":"Yunlong","family":"Ma","sequence":"additional","affiliation":[]},{"given":"Hongyan","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Meihong","family":"Liu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6301-1311","authenticated-orcid":false,"given":"Liang","family":"Zhao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,4,9]]},"reference":[{"key":"935_CR1","first-page":"9649","volume-title":"Advances in Neural Information Processing Systems","author":"R Abboud","year":"2020","unstructured":"Abboud R, Ceylan I, Lukasiewicz T, Salvatori T (2020) Boxe: A box embedding model for knowledge base completion. In: Larochelle H, Ranzato M, Hadsell R, Balcan MF, Lin H (eds) Advances in Neural Information Processing Systems, vol 33. Curran Associates, Canada, pp 9649\u20139661"},{"key":"935_CR2","unstructured":"Bai S, Kolter JZ, Koltun V (2018) An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. CoRR arXiv:1803.01271"},{"key":"935_CR3","doi-asserted-by":"publisher","first-page":"702","DOI":"10.1016\/j.ins.2022.05.079","volume":"606","author":"EP Barracchia","year":"2022","unstructured":"Barracchia EP, Pio G, Bifet A, Gomes HM, Pfahringer B, Ceci M (2022) LP-ROBIN: link prediction in dynamic networks exploiting incremental node embedding. Inf. Sci. 606:702\u2013721. https:\/\/doi.org\/10.1016\/j.ins.2022.05.079","journal-title":"Inf. Sci."},{"key":"935_CR4","unstructured":"Bordes A, Usunier N, Garc\u00eda-Dur\u00e1n A, Weston J, Yakhnenko O (2013) Translating embeddings for modeling multi-relational data. In: Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013, Lake Tahoe, pp. 2787\u20132795"},{"key":"935_CR5","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1016\/j.neucom.2020.03.011","volume":"399","author":"Y Chen","year":"2020","unstructured":"Chen Y, Kang Y, Chen Y, Wang Z (2020) Probabilistic forecasting with temporal convolutional neural network. Neurocomputing 399:491\u2013501. https:\/\/doi.org\/10.1016\/j.neucom.2020.03.011","journal-title":"Neurocomputing"},{"key":"935_CR6","doi-asserted-by":"crossref","unstructured":"Chen J, Chen J, Yu Z (2019) Incorporating structured commonsense knowledge in story completion. Proceedings of the AAAI Conference on Artificial Intelligence, 6244\u20136251","DOI":"10.1609\/aaai.v33i01.33016244"},{"key":"935_CR7","doi-asserted-by":"crossref","unstructured":"Chen M, Zhang W, Zhang W, Chen Q, Chen H (2019) Meta relational learning for few-shot link prediction in knowledge graphs. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, pp. 4216\u20134225. Association for Computational Linguistics, Hong Kong","DOI":"10.18653\/v1\/D19-1431"},{"key":"935_CR8","doi-asserted-by":"crossref","unstructured":"Dasgupta SS, Ray SN, Talukdar PP (2018) Hyte: Hyperplane-based temporally aware knowledge graph embedding. In: Riloff E, Chiang D, Hockenmaier J, Tsujii J (eds.) Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 2001\u20132011. Association for Computational Linguistics, Brussels","DOI":"10.18653\/v1\/D18-1225"},{"key":"935_CR9","unstructured":"Finn C, Abbeel P, Levine S (2017) Model-agnostic meta-learning for fast adaptation of deep networks. In: Proceedings of the 34th International Conference on Machine Learning, ICML 2017. Proceedings of Machine Learning Research, vol. 70, pp. 1126\u20131135. PMLR, Sydney"},{"key":"935_CR10","doi-asserted-by":"crossref","unstructured":"Garc\u00eda-Dur\u00e1n A, Dumancic S, Niepert M (2018) Learning sequence encoders for temporal knowledge graph completion. In: Riloff E, Chiang D, Hockenmaier J, Tsujii J (eds.) Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 4816\u20134821. Association for Computational Linguistics, Brussels","DOI":"10.18653\/v1\/D18-1516"},{"key":"935_CR11","doi-asserted-by":"publisher","unstructured":"Goyal P, Chhetri SR, Canedo A (2020) dyngraph2vec: Capturing network dynamics using dynamic graph representation learning. Knowl. Based Syst. 187. https:\/\/doi.org\/10.1016\/j.knosys.2019.06.024","DOI":"10.1016\/j.knosys.2019.06.024"},{"key":"935_CR12","doi-asserted-by":"crossref","unstructured":"Hao J, Ju CJ-, Chen M, Sun Y, Zaniolo C, Wang W (2020) Bio-joie: Joint representation learning of biological knowledge bases. In: International Conference on Bioinformatics, Computational Biology and Health Informatics, pp. 42\u201314210. ACM, USA","DOI":"10.1101\/2020.06.15.153692"},{"key":"935_CR13","doi-asserted-by":"crossref","unstructured":"He H, Balakrishnan A, Eric M, Liang P (2017) Learning symmetric collaborative dialogue agents with dynamic knowledge graph embeddings. CoRR arXiv:1704.07130","DOI":"10.18653\/v1\/P17-1162"},{"key":"935_CR14","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778. IEEE Computer Society, Las Vegas","DOI":"10.1109\/CVPR.2016.90"},{"key":"935_CR15","unstructured":"Jiang T, Liu T, Ge T, Sha L, Chang B, Li S, Sui Z (2016) Towards time-aware knowledge graph completion. In: Calzolari N, Matsumoto Y, Prasad R (eds.) COLING 2016, 26th International Conference on Computational Linguistics, Proceedings of the Conference: Technical Papers, pp. 1715\u20131724. ACL, Osaka"},{"key":"935_CR16","unstructured":"Jin W, Zhang C, Szekely PA, Ren X (2019) Recurrent event network for reasoning over temporal knowledge graphs. CoRR"},{"issue":"2","key":"935_CR17","first-page":"1","volume":"11","author":"Y Koren","year":"2018","unstructured":"Koren Y, Bell RM, Volinsky C (2018) Matrix factorization techniques for recommender systems. Comput. Inf. Sci. 11(2):1\u201310","journal-title":"Comput. Inf. Sci."},{"key":"935_CR18","first-page":"1771","volume-title":"Companion of the The Web Conference 2018","author":"J Leblay","year":"2018","unstructured":"Leblay J, Chekol MW (2018) Deriving validity time in knowledge graph. In: Champin P, Gandon F, Lalmas M, Ipeirotis PG (eds) Companion of the The Web Conference 2018. ACM, Lyon, pp 1771\u20131776"},{"key":"935_CR19","unstructured":"Leetaru K, Schrodt PA (2013) Gdelt: Global data on events, location, and tone, 1979\u20132012. In: ISA Annual Convention, vol. 2, pp. 1\u201349. Citeseer"},{"key":"935_CR20","doi-asserted-by":"crossref","unstructured":"Liu Z, Xiong C, Sun M, Liu Z (2018) Entity-duet neural ranking: Understanding the role of knowledge graph semantics in neural information retrieval. In: Gurevych I, Miyao Y (eds.) Proceedings of the 56th Annual Meeting of Th Association for Computational Linguistics, pp. 2395\u20132405. ACL, Melbourne","DOI":"10.18653\/v1\/P18-1223"},{"issue":"7","key":"935_CR21","doi-asserted-by":"publisher","first-page":"7779","DOI":"10.1609\/aaai.v36i7.20746","volume":"36","author":"J Messner","year":"2022","unstructured":"Messner J, Abboud R, Ceylan \u0130\u0130 (2022) Temporal knowledge graph completion using box embeddings. Proceedings of the AAAI Conference on Artificial Intelligence 36(7):7779\u20137787. https:\/\/doi.org\/10.1609\/aaai.v36i7.20746","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"935_CR22","volume-title":"3rd Conference on Automated Knowledge Base Construction","author":"M Mirtaheri","year":"2021","unstructured":"Mirtaheri M, Rostami M, Ren X, Morstatter F, Galstyan A (2021) One-shot learning for temporal knowledge graphs. In: Chen D, Berant J, McCallum A, Singh S (eds) 3rd Conference on Automated Knowledge Base Construction. AKBC, Virtual"},{"key":"935_CR23","unstructured":"Ravi S, Larochelle H (2017) Optimization as a model for few-shot learning. In: 5th International Conference on Learning Representations, ICLR 2017. OpenReview.net, Toulon"},{"issue":"7","key":"935_CR24","doi-asserted-by":"publisher","first-page":"6471","DOI":"10.1609\/aaai.v35i7.16802","volume":"35","author":"A Sadeghian","year":"2021","unstructured":"Sadeghian A, Armandpour M, Colas A, Wang DZ (2021) Chronor: Rotation based temporal knowledge graph embedding. Proceedings of the AAAI Conference on Artificial Intelligence 35(7):6471\u20136479. https:\/\/doi.org\/10.1609\/aaai.v35i7.16802","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"935_CR25","unstructured":"Salimans T, Kingma DP (2016) Weight normalization: A simple reparameterization to accelerate training of deep neural networks. In: Lee DD, Sugiyama M, von Luxburg U, Guyon I, Garnett R (eds.) Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems, Barcelona, p. 901"},{"key":"935_CR26","first-page":"593","volume-title":"The Semantic Web - 15th International Conference, ESWC 2018","author":"MS Schlichtkrull","year":"2018","unstructured":"Schlichtkrull MS, Kipf TN, Bloem P, van den Berg R, Titov I, Welling M (2018) Modeling relational data with graph convolutional networks. The Semantic Web - 15th International Conference, ESWC 2018, vol 10843. Springer, Heraklion, pp 593\u2013607"},{"key":"935_CR27","unstructured":"Snell J, Swersky K, Zemel RS (2017) Prototypical networks for few-shot learning. In: Guyon I, von Luxburg U, Bengio S, Wallach HM, Fergus R, Vishwanathan SVN, Garnett R (eds.) Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, Long Beach, pp. 4077\u20134087"},{"key":"935_CR28","unstructured":"Sun Z, Deng Z, Nie J, Tang J (2019) Rotate: Knowledge graph embedding by relational rotation in complex space. In: 7th International Conference on Learning Representations, ICLR 2019. OpenReview.net, New Orleans"},{"key":"935_CR29","unstructured":"Trivedi R, Dai H, Wang Y, Song L (2017) Know-evolve: Deep temporal reasoning for dynamic knowledge graphs. In: Proceedings of the 34th International Conference on Machine Learning, vol. 70, pp. 3462\u20133471. PMLR, Sydney"},{"key":"935_CR30","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. In: Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, Long Beach, pp. 5998\u20136008"},{"key":"935_CR31","unstructured":"Vinyals O, Blundell C, Lillicrap T, Kavukcuoglu K, Wierstra D (2016) Matching networks for one shot learning. In: Lee DD, Sugiyama M, von Luxburg U, Guyon I, Garnett R (eds.) Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems, Barcelona, pp. 3630\u20133638"},{"key":"935_CR32","doi-asserted-by":"crossref","unstructured":"Xiong W, Yu M, Chang S, Guo X, Wang WY (2018) One-shot relational learning for knowledge graphs. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 1980\u20131990. Association for Computational Linguistics, Brussels","DOI":"10.18653\/v1\/D18-1223"},{"key":"935_CR33","volume-title":"3rd International Conference on Learning Representations","author":"B Yang","year":"2015","unstructured":"Yang B, Yih W, He X, Gao J, Deng L (2015) Embedding entities and relations for learning and inference in knowledge bases. In: Bengio Y, LeCun Y (eds) 3rd International Conference on Learning Representations. ICLR, San Diego"},{"issue":"03","key":"935_CR34","doi-asserted-by":"publisher","first-page":"3041","DOI":"10.1609\/aaai.v34i03.5698","volume":"34","author":"C Zhang","year":"2020","unstructured":"Zhang C, Yao H, Huang C, Jiang M, Li Z, Chawla NV (2020) Few-shot knowledge graph completion. Proceedings of the AAAI Conference on Artificial Intelligence 34(03):3041\u20133048","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"935_CR35","doi-asserted-by":"publisher","first-page":"4732","DOI":"10.1609\/aaai.v35i5.16604","volume":"35","author":"C Zhu","year":"2021","unstructured":"Zhu C, Chen M, Fan C, Cheng G, Zhang Y (2021) Learning from history: Modeling temporal knowledge graphs with sequential copy-generation networks. Proceedings of the AAAI Conference on Artificial Intelligence 35:4732\u20134740. https:\/\/doi.org\/10.1609\/aaai.v35i5.16604","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"}],"container-title":["Data Mining and Knowledge Discovery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-023-00935-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10618-023-00935-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-023-00935-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,6]],"date-time":"2023-07-06T17:14:38Z","timestamp":1688663678000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10618-023-00935-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,9]]},"references-count":35,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,7]]}},"alternative-id":["935"],"URL":"https:\/\/doi.org\/10.1007\/s10618-023-00935-7","relation":{},"ISSN":["1384-5810","1573-756X"],"issn-type":[{"type":"print","value":"1384-5810"},{"type":"electronic","value":"1573-756X"}],"subject":[],"published":{"date-parts":[[2023,4,9]]},"assertion":[{"value":"11 March 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 March 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 April 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}