{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T17:05:59Z","timestamp":1772643959557,"version":"3.50.1"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T00:00:00Z","timestamp":1726099200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T00:00:00Z","timestamp":1726099200000},"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":["62276057"],"award-info":[{"award-number":["62276057"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2025,3]]},"DOI":"10.1007\/s13042-024-02378-y","type":"journal-article","created":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T08:20:41Z","timestamp":1726129241000},"page":"2073-2091","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Triple confidence-aware encoder\u2013decoder model for commonsense knowledge graph completion"],"prefix":"10.1007","volume":"16","author":[{"given":"Hongzhi","family":"Chen","sequence":"first","affiliation":[]},{"given":"Fu","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Qinghui","family":"Li","sequence":"additional","affiliation":[]},{"given":"Xiang","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yifan","family":"Ding","sequence":"additional","affiliation":[]},{"given":"Daqing","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Jingwei","family":"Cheng","sequence":"additional","affiliation":[]},{"given":"Xing","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,9,12]]},"reference":[{"key":"2378_CR1","doi-asserted-by":"crossref","unstructured":"Razniewski S, Tandon N, Varde AS (2021) Information to wisdom: commonsense knowledge extraction and compilation. In: Proceedings of the 14th ACM international conference on web search and data mining, pp 1143\u20131146","DOI":"10.1145\/3437963.3441664"},{"key":"2378_CR2","doi-asserted-by":"crossref","unstructured":"Vashishth S, Joshi R, Prayaga SS, Bhattacharyya C, Talukdar P (2018) Reside: improving distantly-supervised neural relation extraction using side information. Preprint arXiv:1812.04361","DOI":"10.18653\/v1\/D18-1157"},{"key":"2378_CR3","doi-asserted-by":"crossref","unstructured":"Bordes A, Chopra S, Weston J (2014) Question answering with subgraph embeddings. Preprint arXiv:1406.3676","DOI":"10.3115\/v1\/D14-1067"},{"key":"2378_CR4","doi-asserted-by":"crossref","unstructured":"Ilievski F, Oltramari A, Ma K, Zhang B, Szekely P (2021) Dimensions of commonsense knowledge. Preprint arXiv:2101.04640","DOI":"10.1016\/j.knosys.2021.107347"},{"issue":"12","key":"2378_CR5","doi-asserted-by":"publisher","first-page":"2724","DOI":"10.1109\/TKDE.2017.2754499","volume":"29","author":"Q Wang","year":"2017","unstructured":"Wang Q, Mao Z, Wang B, Guo L (2017) Knowledge graph embedding: a survey of approaches and applications. IEEE Trans Knowl Data Eng 29(12):2724\u20132743","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2378_CR6","first-page":"1","volume":"26","author":"A Bordes","year":"2013","unstructured":"Bordes A, Usunier N, Garcia-Duran A, Weston J, Yakhnenko O (2013) Translating embeddings for modeling multi-relational data. Adv Neural Inform Process Syst 26:1","journal-title":"Adv Neural Inform Process Syst"},{"key":"2378_CR7","doi-asserted-by":"crossref","unstructured":"Ji G, He S, Xu L, Liu K, Zhao J (2015) Knowledge graph embedding via dynamic mapping matrix. In: Proceedings of the 53rd annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing (volume 1: long papers), pp 687\u2013696","DOI":"10.3115\/v1\/P15-1067"},{"key":"2378_CR8","first-page":"4463","volume":"32","author":"I Balazevic","year":"2019","unstructured":"Balazevic I, Allen C, Hospedales T (2019) Multi-relational Poincar\u00e9 graph embeddings. Adv Neural Inform Process Syst 32:4463\u20134473","journal-title":"Adv Neural Inform Process Syst"},{"key":"2378_CR9","doi-asserted-by":"crossref","unstructured":"Chami I, Wolf A, Sala F, R\u00e9 C (2019) Low-dimensional knowledge graph embeddings via hyperbolic rotations. In: Graph representation learning NeurIPS 2019 workshop","DOI":"10.18653\/v1\/2020.acl-main.617"},{"key":"2378_CR10","unstructured":"Yang B, Yih W-T, He X, Gao J, Deng L (2014) Embedding entities and relations for learning and inference in knowledge bases. Preprint arXiv:1412.6575"},{"key":"2378_CR11","unstructured":"Trouillon T, Welbl J, Riedel S, Gaussier \u00c9, Bouchard G (2016) Complex embeddings for simple link prediction. In: International conference on machine learning, PMLR, pp 2071\u20132080"},{"key":"2378_CR12","doi-asserted-by":"crossref","unstructured":"Shang C, Tang Y, Huang J, Bi J, He X, Zhou B (2019) End-to-end structure-aware convolutional networks for knowledge base completion. In: Proceedings of the AAAI conference on artificial intelligence, vol\u00a033, pp 3060\u20133067","DOI":"10.1609\/aaai.v33i01.33013060"},{"key":"2378_CR13","doi-asserted-by":"crossref","unstructured":"Dettmers T, Minervini P, Stenetorp P, Riedel S (2018) Convolutional 2D knowledge graph embeddings. In: Thirty-second AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v32i1.11573"},{"key":"2378_CR14","doi-asserted-by":"crossref","unstructured":"Nguyen DQ, Nguyen TD, Nguyen DQ, Phung D (2017) A novel embedding model for knowledge base completion based on convolutional neural network. Preprint arXiv:1712.02121","DOI":"10.18653\/v1\/N18-2053"},{"key":"2378_CR15","doi-asserted-by":"crossref","unstructured":"Omeliyanenko J, Zehe A, Hettinger L, Hotho A (2020) Lm4kg: improving common sense knowledge graphs with language models. In: International semantic web conference. Springer, London, pp 456\u2013473","DOI":"10.1007\/978-3-030-62419-4_26"},{"key":"2378_CR16","doi-asserted-by":"crossref","unstructured":"Malaviya C, Bhagavatula C, Bosselut A, Choi Y (2020) Commonsense knowledge base completion with structural and semantic context. In: Proceedings of the AAAI conference on artificial intelligence, vol\u00a034, pp 2925\u20132933","DOI":"10.1609\/aaai.v34i03.5684"},{"key":"2378_CR17","doi-asserted-by":"crossref","unstructured":"Wang B, Wang G, Huang J, You J, Leskovec J, Kuo C-CJ (2021) Inductive learning on commonsense knowledge graph completion. In: 2021 international joint conference on neural networks (IJCNN). IEEE, pp 1\u20138","DOI":"10.1109\/IJCNN52387.2021.9534355"},{"key":"2378_CR18","doi-asserted-by":"crossref","unstructured":"Li X, Taheri A, Tu L, Gimpel K (2016) Commonsense knowledge base completion. In: Proceedings of the 54th annual meeting of the association for computational linguistics (volume 1: long papers), pp 1445\u20131455","DOI":"10.18653\/v1\/P16-1137"},{"key":"2378_CR19","doi-asserted-by":"crossref","unstructured":"Sap M, Le\u00a0Bras R, Allaway E, Bhagavatula C, Lourie N, Rashkin H, Roof B, Smith NA, Choi Y (2019) Atomic: an atlas of machine commonsense for if\u2013then reasoning. In: Proceedings of the AAAI conference on artificial intelligence, vol\u00a033, pp 3027\u20133035","DOI":"10.1609\/aaai.v33i01.33013027"},{"key":"2378_CR20","doi-asserted-by":"crossref","unstructured":"Bollacker K, Evans C, Paritosh P, Sturge T, Taylor J (2008) Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of the 2008 ACM SIGMOD international conference on management of data, pp 1247\u20131250","DOI":"10.1145\/1376616.1376746"},{"key":"2378_CR21","doi-asserted-by":"crossref","unstructured":"Pujara J, Augustine E, Getoor L (2017) Sparsity and noise: where knowledge graph embeddings fall short. In: Proceedings of the 2017 conference on empirical methods in natural language processing, pp 1751\u20131756","DOI":"10.18653\/v1\/D17-1184"},{"key":"2378_CR22","unstructured":"Devlin J, Chang M-W, Lee K, Toutanova K (2018) Bert: pre-training of deep bidirectional transformers for language understanding. Preprint arXiv:1810.04805"},{"key":"2378_CR23","unstructured":"Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. In: 5th international conference on learning representations, ICLR 2017, Toulon, France, April 24\u201326, 2017, conference track proceedings"},{"key":"2378_CR24","unstructured":"Velickovic P, Cucurull G, Casanova A, Romero A, Li\u00f2 P, Bengio Y (2018) Graph attention networks. In: 6th international conference on learning representations (ICLR)"},{"key":"2378_CR25","doi-asserted-by":"crossref","unstructured":"Schlichtkrull M, Kipf TN, Bloem P, Van Den\u00a0Berg R, Titov I, Welling M (2018) Modeling relational data with graph convolutional networks. In: European semantic web conference. Springer, London, pp 593\u2013607","DOI":"10.1007\/978-3-319-93417-4_38"},{"key":"2378_CR26","doi-asserted-by":"crossref","unstructured":"Vashishth S, Sanyal S, Nitin V, Agrawal N, Talukdar P (2020) Interacte: improving convolution-based knowledge graph embeddings by increasing feature interactions. In: Proceedings of the AAAI conference on artificial intelligence, vol\u00a034, pp 3009\u20133016","DOI":"10.1609\/aaai.v34i03.5694"},{"key":"2378_CR27","doi-asserted-by":"publisher","first-page":"109597","DOI":"10.1016\/j.knosys.2022.109597","volume":"2022","author":"T Shen","year":"2022","unstructured":"Shen T, Zhang F, Cheng J (2022) A comprehensive overview of knowledge graph completion. Knowl Based Syst 2022:109597","journal-title":"Knowl Based Syst"},{"key":"2378_CR28","doi-asserted-by":"crossref","unstructured":"Saito I, Nishida K, Asano H, Tomita J (2018) Commonsense knowledge base completion and generation. In: Proceedings of the 22nd conference on computational natural language learning, pp 141\u2013150","DOI":"10.18653\/v1\/K18-1014"},{"key":"2378_CR29","doi-asserted-by":"crossref","unstructured":"Bosselut A, Rashkin H, Sap M, Malaviya C, Celikyilmaz A, Choi Y (2019) Comet: Commonsense transformers for automatic knowledge graph construction. Preprint arXiv:1906.05317","DOI":"10.18653\/v1\/P19-1470"},{"key":"2378_CR30","doi-asserted-by":"crossref","unstructured":"Lovelace J, Newman-Griffis D, Vashishth S, Lehman JF, Ros\u00e9 CP (2021) Robust knowledge graph completion with stacked convolutions and a student re-ranking network. In: Proceedings of the conference. Association for Computational Linguistics. Meeting, vol 2021, NIH Public Access, p 1016","DOI":"10.18653\/v1\/2021.acl-long.82"},{"key":"2378_CR31","doi-asserted-by":"crossref","unstructured":"Speer R, Havasi C (2013) Conceptnet 5: a large semantic network for relational knowledge. In: The people\u2019s web meets NLP. Springer, London, pp 161\u2013176","DOI":"10.1007\/978-3-642-35085-6_6"},{"key":"2378_CR32","unstructured":"Gilmer J, Schoenholz SS, Riley PF, Vinyals O, Dahl GE (2017) Neural message passing for quantum chemistry. In: International conference on machine learning, PMLR, pp 1263\u20131272"},{"key":"2378_CR33","unstructured":"Ruffinelli D, Broscheit S, Gemulla R (2019) You can teach an old dog new tricks! on training knowledge graph embeddings. In: International conference on learning representations"},{"key":"2378_CR34","unstructured":"Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. Preprint arXiv:1412.6980"},{"key":"2378_CR35","unstructured":"Loshchilov I, Hutter F (2019) Decoupled weight decay regularization. Preprint published January 4"},{"issue":"11","key":"2378_CR36","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1145\/219717.219748","volume":"38","author":"GA Miller","year":"1995","unstructured":"Miller GA (1995) Wordnet: a lexical database for English. Commun ACM 38(11):39\u201341","journal-title":"Commun ACM"},{"issue":"9","key":"2378_CR37","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1145\/2701413","volume":"58","author":"E Davis","year":"2015","unstructured":"Davis E, Marcus G (2015) Commonsense reasoning and commonsense knowledge in artificial intelligence. Commun ACM 58(9):92\u2013103","journal-title":"Commun ACM"},{"key":"2378_CR38","unstructured":"Storks S, Gao Q, Chai JY (2019) Recent advances in natural language inference: a survey of benchmarks, resources, and approaches. Preprint arXiv:1904.01172"},{"key":"2378_CR39","doi-asserted-by":"crossref","unstructured":"Cambria E, Song Y, Wang H, Hussain A (2011) Isanette: a common and common sense knowledge base for opinion mining. In: 2011 IEEE 11th international conference on data mining workshops. IEEE, pp 315\u2013322","DOI":"10.1109\/ICDMW.2011.106"},{"key":"2378_CR40","doi-asserted-by":"crossref","unstructured":"Speer R, Chin J, Havasi C (2017) Conceptnet 5.5: an open multilingual graph of general knowledge. In: Thirty-first AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v31i1.11164"},{"key":"2378_CR41","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.110219","volume":"261","author":"S Rahmani","year":"2023","unstructured":"Rahmani S, Hosseini S, Zall R, Kangavari MR, Kamran S, Hua W (2023) Transfer-based adaptive tree for multimodal sentiment analysis based on user latent aspects. Knowl Based Syst 261:110219","journal-title":"Knowl Based Syst"},{"key":"2378_CR42","doi-asserted-by":"crossref","unstructured":"Li B, Zheng C, Giancola S, Ghanem B (2022) SCTN: sparse convolution-transformer network for scene flow estimation. In: Proceedings of the AAAI conference on artificial intelligence, vol\u00a036, pp 1254\u20131262","DOI":"10.1609\/aaai.v36i2.20012"},{"issue":"4","key":"2378_CR43","first-page":"1","volume":"40","author":"H Peng","year":"2021","unstructured":"Peng H, Zhang R, Dou Y, Yang R, Zhang J, Yu PS (2021) Reinforced neighborhood selection guided multi-relational graph neural networks. ACM Trans Inform Syst (TOIS) 40(4):1\u201346","journal-title":"ACM Trans Inform Syst (TOIS)"},{"key":"2378_CR44","first-page":"1","volume":"2024","author":"H Peng","year":"2024","unstructured":"Peng H, Zhang J, Huang X, Hao Z, Li A, Yu Z, Yu PS (2024) Unsupervised social bot detection via structural information theory. ACM Trans Inform Syst 2024:1","journal-title":"ACM Trans Inform Syst"},{"key":"2378_CR45","unstructured":"Cao Y, Peng H, Li A, You C, Hao Z, Yu PS (2024) Multi-relational structural entropy. In: The 40th conference on uncertainty in artificial intelligence"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02378-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-024-02378-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02378-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,19]],"date-time":"2025-02-19T18:29:30Z","timestamp":1739989770000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-024-02378-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,12]]},"references-count":45,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,3]]}},"alternative-id":["2378"],"URL":"https:\/\/doi.org\/10.1007\/s13042-024-02378-y","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,12]]},"assertion":[{"value":"1 November 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 September 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 September 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All the authors declare there are no any Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}