{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T15:00:11Z","timestamp":1776956411137,"version":"3.51.4"},"reference-count":26,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2023,3,4]],"date-time":"2023-03-04T00:00:00Z","timestamp":1677888000000},"content-version":"vor","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61402087"],"award-info":[{"award-number":["61402087"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003787","name":"Natural Science Foundation of Hebei Province","doi-asserted-by":"publisher","award":["F2022501015"],"award-info":[{"award-number":["F2022501015"]}],"id":[{"id":"10.13039\/501100003787","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Project of Scientific Research Funds in Colleges and Universities","award":["ZD2020402"],"award-info":[{"award-number":["ZD2020402"]}]},{"name":"Program for 333 Talents in Hebei Province","award":["A202001066"],"award-info":[{"award-number":["A202001066"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,3,10]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>With the deepening of the research on knowledge graph embedding, temporal knowledge graphs (TKGs), which are dynamic changes over time, have gradually gained the attention of researchers. Although some TKG-embedding models have been proposed, they did not perform well for certain relationships with insufficient samples, since they all require tremendous training samples. Thus, few-shot link prediction tasks, namely predicting new relation-specific quadruples by observing only a few samples, are still very challenging. In this paper, a method named meta-reasoning for TKGs (MetaRT) is proposed to solve this universal but challenging problem. MetaRT works by extracting the meta-information of a specific relation and updating it quickly, so that the model can learn the most critical information in TKG swiftly and independently. In the meantime, temporal information can be managed well by a TKG learner. Finally, through a large number of experiments, it shows that MetaRT outperforms other existing TKG-embedding models on the problem of few-shot learning.<\/jats:p>","DOI":"10.1093\/jcde\/qwad016","type":"journal-article","created":{"date-parts":[[2023,3,4]],"date-time":"2023-03-04T12:28:33Z","timestamp":1677932913000},"page":"711-721","source":"Crossref","is-referenced-by-count":6,"title":["Few-shot link prediction with meta-learning for temporal knowledge graphs"],"prefix":"10.1093","volume":"10","author":[{"given":"Lin","family":"Zhu","sequence":"first","affiliation":[{"name":"School of Computer and Communication Engineering, Northeastern University (Qinhuangdao) , Qinhuangdao 066004 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yizong","family":"Xing","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, The University of Melbourne , Melbourne, VIC, 3010 , Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9546-3208","authenticated-orcid":false,"given":"Luyi","family":"Bai","sequence":"additional","affiliation":[{"name":"School of Computer and Communication Engineering, Northeastern University (Qinhuangdao) , Qinhuangdao 066004 , China"},{"name":"School of Informatics, University of Leicester , Leicester LE1 7RH , UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiwen","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science, Carnegie Mellon University , Pittsburgh , PA 15213 , USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2023,3,3]]},"reference":[{"key":"2023040316122958500_","article-title":"Learning to learn by gradient descent by gradient descent","volume-title":"Proceedings of the 30th International Conference on Neural Information Processing Systems","author":"Andrychowicz","year":"2016"},{"key":"2023040316122958500_","article-title":"FTMF: Few-shot temporal knowledge graph completion based on meta-optimization and fault-tolerant mechanism","author":"Bai","year":"2022","journal-title":"World Wide Web Journal"},{"key":"2023040316122958500_","article-title":"Translating embeddings for modeling multi-relational data","volume-title":"Proceedings of the Conference on Neural Information Processing Systems","author":"Bordes","year":"2013"},{"key":"2023040316122958500_","article-title":"ICEWS coded event data","author":"Boschee","year":"2015"},{"key":"2023040316122958500_","doi-asserted-by":"crossref","DOI":"10.18653\/v1\/D19-1431","article-title":"Meta relational learning for few-shot link prediction in knowledge graphs","volume-title":"Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing","author":"Chen","year":"2019"},{"key":"2023040316122958500_","doi-asserted-by":"crossref","DOI":"10.18653\/v1\/D18-1225","article-title":"Hyte: Hyperplane-based temporally aware knowledge graph embedding","volume-title":"Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing","author":"Dasgupta","year":"2018"},{"key":"2023040316122958500_","article-title":"Model-agnostic meta-learning for fast adaptation of deep networks","volume-title":"Proceedings of the 34th International Conference on Machine Learning","author":"Finn","year":"2017"},{"key":"2023040316122958500_","doi-asserted-by":"crossref","DOI":"10.18653\/v1\/D18-1516","article-title":"Learning sequence encoders for temporal knowledge graph completion","volume-title":"Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing","author":"Garc\u00eda-Dur\u00e1n","year":"2018"},{"key":"2023040316122958500_","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":"2023040316122958500_","doi-asserted-by":"crossref","DOI":"10.18653\/v1\/2020.emnlp-main.541","article-title":"Recurrent event network: Autoregressive structure inferenceover temporal knowledge graphs","volume-title":"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing","author":"Jin","year":"2020"},{"key":"2023040316122958500_","article-title":"Adam: A method for stochastic optimization","volume-title":"Proceedings of the 3rd International Conference on Learning Representations","author":"Kingma","year":"2015"},{"key":"2023040316122958500_","doi-asserted-by":"crossref","first-page":"117036","DOI":"10.1016\/j.eswa.2022.117036","article-title":"Block term decomposition with distinct time granularities for temporal knowledge graph completion","volume":"201","author":"Lai","year":"2022","journal-title":"Expert Systems with Applications"},{"key":"2023040316122958500_","doi-asserted-by":"crossref","DOI":"10.1145\/3184558.3191639","article-title":"Deriving validity time in knowledge graph","volume-title":"Proceedings of the 27th Web Conference","author":"Leblay","year":"2018"},{"key":"2023040316122958500_","doi-asserted-by":"crossref","DOI":"10.1609\/aaai.v29i1.9491","article-title":"Learning entity and relation embeddings for knowledge graph completion","volume-title":"Proceedings of the 29th AAAI Conference on Artificial Intelligence","author":"Lin","year":"2015"},{"key":"2023040316122958500_","article-title":"One-shot learning for temporal knowledge graphs","volume-title":"Proceedings of the 3rd Conference on Automated Knowledge Base Construction","author":"Mirtaheri","year":"2021"},{"key":"2023040316122958500_","article-title":"Meta networks","volume-title":"Proceedings of the 34th International Conference on Machine Learning","author":"Munkhdalai","year":"2017"},{"key":"2023040316122958500_","article-title":"Meta-learning with memory-augmented neural networks","volume-title":"Proceedings of the 33rd International Conference on International Conference on Machine Learning","author":"Santoro","year":"2016"},{"key":"2023040316122958500_","article-title":"Prototypical networks for few-shot learning","volume-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems","author":"Snell","year":"2017"},{"key":"2023040316122958500_","article-title":"Know-Evolve: Deep temporal reasoning for dynamic knowledge graphs","volume-title":"Proceedings of the 34th International Conference on Machine Learning","author":"Trivedi","year":"2017"},{"key":"2023040316122958500_","article-title":"Matching networks for one shot learning","volume-title":"Proceedings of the 30th International Conference on Neural Information Processing Systems","author":"Vinyals","year":"2016"},{"key":"2023040316122958500_","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1145\/2629489","article-title":"Wikidata: A free collaborative knowledgebase","volume":"57","author":"Vrande\u010di\u0107","year":"2014","journal-title":"Communications of the ACM"},{"key":"2023040316122958500_","doi-asserted-by":"crossref","DOI":"10.1609\/aaai.v28i1.8870","article-title":"Knowledge graph embedding by translating on hyperplanes","volume-title":"Proceedings of the 28th AAAI Conference on Artificial Intelligence","author":"Wang","year":"2014"},{"key":"2023040316122958500_","doi-asserted-by":"crossref","DOI":"10.18653\/v1\/2020.emnlp-main.462","article-title":"TeMP: Temporal message passing for temporal knowledge graph completion","volume-title":"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)","author":"Wu","year":"2020"},{"key":"2023040316122958500_","doi-asserted-by":"crossref","DOI":"10.18653\/v1\/D18-1223","article-title":"One-shot relational learning for knowledge graphs","volume-title":"Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing","author":"Xiong","year":"2018"},{"key":"2023040316122958500_","doi-asserted-by":"crossref","DOI":"10.18653\/v1\/2021.findings-emnlp.35","article-title":"P-INT: A path-based interaction model for few-shot knowledge graph completion","volume-title":"Proceedings of the Association for Computational Linguistics. EMNLP 2021","author":"Xu","year":"2021"},{"key":"2023040316122958500_","doi-asserted-by":"crossref","DOI":"10.1609\/aaai.v34i03.5698","article-title":"Few-shot knowledge graph completion","volume-title":"Proceedings of the 34th AAAI Conference on Artificial Intelligence","author":"Zhang","year":"2020"}],"container-title":["Journal of Computational Design and Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/jcde\/advance-article-pdf\/doi\/10.1093\/jcde\/qwad016\/49419387\/qwad016.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/jcde\/article-pdf\/10\/2\/711\/49732975\/qwad016.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/jcde\/article-pdf\/10\/2\/711\/49732975\/qwad016.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,8]],"date-time":"2023-12-08T00:46:53Z","timestamp":1701996413000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/jcde\/article\/10\/2\/711\/7069330"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,3]]},"references-count":26,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023,3,10]]}},"URL":"https:\/\/doi.org\/10.1093\/jcde\/qwad016","relation":{},"ISSN":["2288-5048"],"issn-type":[{"value":"2288-5048","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2023,4]]},"published":{"date-parts":[[2023,3,3]]}}}