{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T10:35:06Z","timestamp":1763202906243,"version":"3.37.3"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"32","license":[{"start":{"date-parts":[[2024,8,12]],"date-time":"2024-08-12T00:00:00Z","timestamp":1723420800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,8,12]],"date-time":"2024-08-12T00:00:00Z","timestamp":1723420800000},"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":["62276154"],"award-info":[{"award-number":["62276154"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003453","name":"Natural Science Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2023A1515012914"],"award-info":[{"award-number":["2023A1515012914"]}],"id":[{"id":"10.13039\/501100003453","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Basic Research Fund of Shenzhen City","award":["JCYJ20210324120012033","JSGG20210802154402007"],"award-info":[{"award-number":["JCYJ20210324120012033","JSGG20210802154402007"]}]},{"name":"Major Key Project of PCL for Experiments and Applications","award":["PCL2021A06"],"award-info":[{"award-number":["PCL2021A06"]}]},{"name":"Overseas Cooperation Research Fund of Tsinghua Shenzhen International Graduate School","award":["HW2021008"],"award-info":[{"award-number":["HW2021008"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2024,11]]},"DOI":"10.1007\/s00521-024-10276-1","type":"journal-article","created":{"date-parts":[[2024,8,12]],"date-time":"2024-08-12T10:02:55Z","timestamp":1723456975000},"page":"20387-20400","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Advancing entity alignment with dangling cases: a structure-aware approach through optimal transport learning and contrastive learning"],"prefix":"10.1007","volume":"36","author":[{"given":"Jin","family":"Xu","sequence":"first","affiliation":[]},{"given":"Yangning","family":"Li","sequence":"additional","affiliation":[]},{"given":"Xiangjin","family":"Xie","sequence":"additional","affiliation":[]},{"given":"Niu","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Yinghui","family":"Li","sequence":"additional","affiliation":[]},{"given":"Hai-Tao","family":"Zheng","sequence":"additional","affiliation":[]},{"given":"Yong","family":"Jiang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,8,12]]},"reference":[{"key":"10276_CR1","first-page":"1939","volume":"39","author":"Y Gao","year":"2018","unstructured":"Gao Y, Liang J, Han B, Yakout M, Mohamed A (2018) Building a large-scale, accurate and fresh knowledge graph. KDD-2018, Tutorial 39:1939\u20131374","journal-title":"KDD-2018, Tutorial"},{"key":"10276_CR2","doi-asserted-by":"crossref","unstructured":"Cao Y, Liu Z, Li C, Li J, Chua T-S (2019) Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898","DOI":"10.18653\/v1\/P19-1140"},{"key":"10276_CR3","doi-asserted-by":"crossref","unstructured":"Chen M, Tian Y, Chang K-W, Skiena S, Zaniolo C (2018) Co-training embeddings of knowledge graphs and entity descriptions for cross-lingual entity alignment. arXiv preprint arXiv:1806.06478","DOI":"10.24963\/ijcai.2018\/556"},{"key":"10276_CR4","doi-asserted-by":"crossref","unstructured":"He F, Li Z, Qiang Y, Liu A, Liu G, Zhao P, Zhao L, Zhang M, Chen Z (2019) Unsupervised entity alignment using attribute triples and relation triples. In: International conference on database systems for advanced applications. Springer, pp 367\u2013382","DOI":"10.1007\/978-3-030-18576-3_22"},{"key":"10276_CR5","doi-asserted-by":"crossref","unstructured":"Sun Z, Hu W, Zhang Q, Qu Y (2018) Bootstrapping entity alignment with knowledge graph embedding. In: IJCAI, vol 18, pp 4396\u20134402","DOI":"10.24963\/ijcai.2018\/611"},{"key":"10276_CR6","doi-asserted-by":"crossref","unstructured":"Chen M, Tian Y, Yang, M, Zaniolo C (2016) Multilingual knowledge graph embeddings for cross-lingual knowledge alignment. arXiv preprint arXiv:1611.03954","DOI":"10.24963\/ijcai.2017\/209"},{"key":"10276_CR7","unstructured":"Guo L, Chen Z, Chen J, Chen H (2023) Revisit and outstrip entity alignment: a perspective of generative models. arXiv preprint arXiv:2305.14651"},{"key":"10276_CR8","unstructured":"Chen Z, Zhang Y, Fang Y, Geng Y, Guo L, Chen X, Li Q, Zhang W, Chen J, Zhu Y et al (2024) Knowledge graphs meet multi-modal learning: a comprehensive survey. arXiv preprint arXiv:2402.05391"},{"key":"10276_CR9","doi-asserted-by":"crossref","unstructured":"Sun Z, Chen M, Hu W (2021) Knowing the no-match: entity alignment with dangling cases. arXiv preprint arXiv:2106.02248","DOI":"10.18653\/v1\/2021.acl-long.278"},{"key":"10276_CR10","doi-asserted-by":"crossref","unstructured":"Zhang Z, Chen J, Chen X, Liu H, Xiang Y, Liu B, Zheng Y (2020) An industry evaluation of embedding-based entity alignment. arXiv preprint arXiv:2010.11522","DOI":"10.18653\/v1\/2020.coling-industry.17"},{"key":"10276_CR11","doi-asserted-by":"crossref","unstructured":"Liu Z, Cao Y, Pan L, Li J, Chua T-S (2020) Exploring and evaluating attributes, values, and structures for entity alignment. arXiv preprint arXiv:2010.03249","DOI":"10.18653\/v1\/2020.emnlp-main.515"},{"key":"10276_CR12","unstructured":"Chaurasiya D, Surisetty A, Kumar N, Singh A, Dey V, Malhotra A, Dhama G, Arora A (2022) Entity alignment for knowledge graphs: progress, challenges, and empirical studies. arXiv preprint arXiv:2205.08777"},{"key":"10276_CR13","doi-asserted-by":"crossref","unstructured":"Gao Y, Liu X, Wu J, Li T, Wang P, Chen L (2022) Clusterea: scalable entity alignment with stochastic training and normalized mini-batch similarities. In: Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining, pp 421\u2013431","DOI":"10.1145\/3534678.3539331"},{"key":"10276_CR14","doi-asserted-by":"crossref","unstructured":"Sun Z, Wang C, Hu W, Chen M, Dai J, Zhang W, Qu Y (2020) Knowledge graph alignment network with gated multi-hop neighborhood aggregation. In: Proceedings of the AAAI conference on artificial intelligence, vol 34, pp 222\u2013229","DOI":"10.1609\/aaai.v34i01.5354"},{"key":"10276_CR15","doi-asserted-by":"crossref","unstructured":"Luo S, Yu S (2022) An accurate unsupervised method for joint entity alignment and dangling entity detection. arXiv preprint arXiv:2203.05147","DOI":"10.18653\/v1\/2022.findings-acl.183"},{"key":"10276_CR16","unstructured":"Luo S, Cheng P, Yu S (2022) Semi-constraint optimal transport for entity alignment with dangling cases. arXiv preprint arXiv:2203.05744"},{"key":"10276_CR17","doi-asserted-by":"publisher","unstructured":"Li Y, Chen J, Li Y, Xiang Y, Chen X, Zheng H (2023) Vision, deduction and alignment: an empirical study on multi-modal knowledge graph alignment. CoRR abs\/2302.08774. https:\/\/doi.org\/10.48550\/arXiv.2302.08774","DOI":"10.48550\/arXiv.2302.08774"},{"key":"10276_CR18","doi-asserted-by":"crossref","unstructured":"Sun Z, Zhang Q, Hu W, Wang C, Chen M, Akrami F, Li C (2020) A benchmarking study of embedding-based entity alignment for knowledge graphs. arXiv preprint arXiv:2003.07743","DOI":"10.14778\/3407790.3407828"},{"issue":"7","key":"10276_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.patter.2022.100520","volume":"3","author":"R Liu","year":"2022","unstructured":"Liu R, Li Y, Tao L, Liang D, Zheng H (2022) Are we ready for a new paradigm shift? A survey on visual deep MLP. Patterns 3(7):100520. https:\/\/doi.org\/10.1016\/j.patter.2022.100520","journal-title":"Patterns"},{"key":"10276_CR20","doi-asserted-by":"publisher","unstructured":"Li Y, Chen J, Li Y, Yu T, Chen X, Zheng H (2022) Embracing ambiguity: improving similarity-oriented tasks with contextual synonym knowledge. CoRR abs\/2211.10997. https:\/\/doi.org\/10.48550\/arXiv.2211.10997","DOI":"10.48550\/arXiv.2211.10997"},{"key":"10276_CR21","doi-asserted-by":"publisher","unstructured":"Li Y, Li Y, Chen X, Zheng H, Shen Y, Kim H (2022) Active relation discovery: Towards general and label-aware open relation extraction. CoRR abs\/2211.04215. https:\/\/doi.org\/10.48550\/arXiv.2211.04215","DOI":"10.48550\/arXiv.2211.04215"},{"key":"10276_CR22","unstructured":"Bordes A, Usunier N, Garcia-Duran A, Weston J, Yakhnenko O (2013) Translating embeddings for modeling multi-relational data. Adv Neural Inf Process Syst 26"},{"key":"10276_CR23","doi-asserted-by":"crossref","unstructured":"Wang Z, Zhang J, Feng J, Chen Z (2014) Knowledge graph embedding by translating on hyperplanes. In: Proceedings of the AAAI conference on artificial intelligence, vol 28","DOI":"10.1609\/aaai.v28i1.8870"},{"key":"10276_CR24","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":"10276_CR25","unstructured":"Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907"},{"key":"10276_CR26","first-page":"20","volume":"1050","author":"P Velickovic","year":"2017","unstructured":"Velickovic P, Cucurull G, Casanova A, Romero A, Lio P, Bengio Y (2017) Are we ready for a new paradigm shift? A survey on visual deep MLP. stat 1050:20","journal-title":"stat"},{"key":"10276_CR27","doi-asserted-by":"crossref","unstructured":"Wang Z, Lv Q, Lan X, Zhang Y (2018) Cross-lingual knowledge graph alignment via graph convolutional networks. In: Proceedings of the 2018 conference on empirical methods in natural language processing, pp 349\u2013357","DOI":"10.18653\/v1\/D18-1032"},{"key":"10276_CR28","doi-asserted-by":"crossref","unstructured":"Mao X, Wang W, Xu H, Wu Y, Lan M (2020) Relational reflection entity alignment. In: Proceedings of the 29th ACM international conference on information & knowledge management, pp 1095\u20131104","DOI":"10.1145\/3340531.3412001"},{"key":"10276_CR29","doi-asserted-by":"publisher","unstructured":"Li Y, Huang S, Zhang X, Zhou Q, Li Y, Liu R, Cao Y, Zheng H, Shen Y (2022) Automatic context pattern generation for entity set expansion. CoRR abs\/2207.08087. https:\/\/doi.org\/10.48550\/arXiv.2207.08087","DOI":"10.48550\/arXiv.2207.08087"},{"key":"10276_CR30","doi-asserted-by":"crossref","unstructured":"Mao X, Wang W, Xu H, Lan M, Wu Y (2020) Mraea: an efficient and robust entity alignment approach for cross-lingual knowledge graph. In: Proceedings of the 13th international conference on web search and data mining, pp 420\u2013428","DOI":"10.1145\/3336191.3371804"},{"key":"10276_CR31","doi-asserted-by":"crossref","unstructured":"Xu K, Wang L, Yu M, Feng Y, Song Y, Wang Z, Yu D (2019) Cross-lingual knowledge graph alignment via graph matching neural network. arXiv preprint arXiv:1905.11605","DOI":"10.18653\/v1\/P19-1304"},{"key":"10276_CR32","doi-asserted-by":"crossref","unstructured":"Wu Y, Liu X, Feng Y, Wang Z, Zhao D (2019) Jointly learning entity and relation representations for entity alignment. arXiv preprint arXiv:1909.09317","DOI":"10.18653\/v1\/D19-1023"},{"key":"10276_CR33","doi-asserted-by":"crossref","unstructured":"Wu Y, Liu X, Feng Y, Wang Z, Yan R, Zhao D (2019) Relation-aware entity alignment for heterogeneous knowledge graphs. arXiv preprint arXiv:1908.08210","DOI":"10.24963\/ijcai.2019\/733"},{"key":"10276_CR34","doi-asserted-by":"crossref","unstructured":"Liu X, Hong H, Wang X, Chen Z, Kharlamov E, Dong Y, Tang J (2022) Selfkg: self-supervised entity alignment in knowledge graphs. In: Proceedings of the ACM web conference 2022, pp 860\u2013870","DOI":"10.1145\/3485447.3511945"},{"key":"10276_CR35","doi-asserted-by":"crossref","unstructured":"Tang J, Zhao K, Li J (2023) A fused gromov-wasserstein framework for unsupervised knowledge graph entity alignment. arXiv preprint arXiv:2305.06574","DOI":"10.18653\/v1\/2023.findings-acl.205"},{"key":"10276_CR36","doi-asserted-by":"crossref","unstructured":"Liu X, Wu J, Li T, Chen L, Gao Y (2023) Unsupervised entity alignment for temporal knowledge graphs. In: Proceedings of the ACM web conference 2023, pp 2528\u20132538","DOI":"10.1145\/3543507.3583381"},{"issue":"8","key":"10276_CR37","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1145\/3554727","volume":"55","author":"C Dong","year":"2023","unstructured":"Dong C, Li Y, Gong H, Chen M, Li J, Shen Y, Yang M (2023) A survey of natural language generation. ACM Comput Surv 55(8):173\u2013117338. https:\/\/doi.org\/10.1145\/3554727","journal-title":"ACM Comput Surv"},{"key":"10276_CR38","doi-asserted-by":"publisher","unstructured":"Li Y, Zhou Q, Li Y, Li Z, Liu R, Sun R, Wang Z, Li C, Cao Y, Zheng H-T (2022) The past mistake is the future wisdom: error-driven contrastive probability optimization for Chinese spell checking. In: Findings of the association for computational linguistics: ACL 2022, pp. 3202\u20133213. Association for Computational Linguistics, Dublin, Ireland. https:\/\/doi.org\/10.18653\/v1\/2022.findings-acl.252","DOI":"10.18653\/v1\/2022.findings-acl.252"},{"key":"10276_CR39","doi-asserted-by":"publisher","unstructured":"Li Y, Li Y, He Y, Yu T, Shen Y, Zheng H (2022) Contrastive learning with hard negative entities for entity set expansion. In: Amig\u00f3 E, Castells P, Gonzalo J, Carterette B, Culpepper JS, Kazai G (eds) SIGIR \u201922: The 45th international ACM SIGIR conference on research and development in information retrieval, Madrid, Spain, July 11\u201315, 2022, pp 1077\u20131086. ACM. https:\/\/doi.org\/10.1145\/3477495.3531954","DOI":"10.1145\/3477495.3531954"},{"key":"10276_CR40","doi-asserted-by":"crossref","unstructured":"Sun Z, Hu W, Li C (2017) Cross-lingual entity alignment via joint attribute-preserving embedding. In: International semantic web conference. Springer, pp 628\u2013644","DOI":"10.1007\/978-3-319-68288-4_37"},{"key":"10276_CR41","doi-asserted-by":"crossref","unstructured":"Mao X, Wang W, Wu Y, Lan M (2021) Boosting the speed of entity alignment 10$$\\times $$: dual attention matching network with normalized hard sample mining. In: Proceedings of the web conference 2021, pp 821\u2013832","DOI":"10.1145\/3442381.3449897"},{"key":"10276_CR42","unstructured":"Srivastava RK, Greff K, Schmidhuber J (2015) Highway networks. arXiv preprint arXiv:1505.00387"},{"key":"10276_CR43","unstructured":"Robinson J, Chuang C-Y, Sra S, Jegelka S (2020) Contrastive learning with hard negative samples. arXiv preprint arXiv:2010.04592"},{"key":"10276_CR44","unstructured":"Conneau A, Lample G, Ranzato M, Denoyer L, J\u00e9gou H (2017) Word translation without parallel data. arXiv preprint arXiv:1710.04087"},{"issue":"2","key":"10276_CR45","doi-asserted-by":"publisher","first-page":"167","DOI":"10.3233\/SW-140134","volume":"6","author":"J Lehmann","year":"2015","unstructured":"Lehmann J, Isele R, Jakob M, Jentzsch A, Kontokostas D, Mendes PN, Hellmann S, Morsey M, Van Kleef P, Auer S et al (2015) Dbpedia-a large-scale, multilingual knowledge base extracted from wikipedia. Seman Web 6(2):167\u2013195","journal-title":"Seman Web"},{"issue":"suppl-1","key":"10276_CR46","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1093\/nar\/gkh061","volume":"32","author":"O Bodenreider","year":"2004","unstructured":"Bodenreider O (2004) The unified medical language system (umls): integrating biomedical terminology. Nucl Acids Res 32(suppl-1):267\u2013270","journal-title":"Nucl Acids Res"},{"key":"10276_CR47","unstructured":"Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the thirteenth international conference on artificial intelligence and statistics. JMLR Workshop and Conference Proceedings, pp 249\u2013256"},{"key":"10276_CR48","unstructured":"Feng F, Yang Y, Cer D, Arivazhagan N, Wang W (2020) Language-agnostic bert sentence embedding. arXiv preprint arXiv:2007.01852"},{"key":"10276_CR49","doi-asserted-by":"crossref","unstructured":"Li C, Cao Y, Hou L, Shi J, Li J, Chua T-S (2019) Semi-supervised entity alignment via joint knowledge embedding model and cross-graph model. Assoc Comput Linguist","DOI":"10.18653\/v1\/D19-1274"},{"key":"10276_CR50","unstructured":"Guo L, Sun Z, Hu W (2019) Learning to exploit long-term relational dependencies in knowledge graphs. In: International conference on machine learning. PMLR, pp 2505\u20132514"},{"key":"10276_CR51","doi-asserted-by":"crossref","unstructured":"Zhu Q, Zhou X, Wu J, Tan J, Guo L (2019) Neighborhood-aware attentional representation for multilingual knowledge graphs. In: Ijcai, pp 1943\u20131949","DOI":"10.24963\/ijcai.2019\/269"},{"key":"10276_CR52","doi-asserted-by":"crossref","unstructured":"Sun Z, Huang J, Hu W, Chen M, Guo L, Qu Y (2019) Transedge: translating relation-contextualized embeddings for knowledge graphs. In: The Semantic Web\u2013ISWC 2019: 18th International Semantic Web Conference, Auckland, New Zealand, October 26\u201330, 2019, Proceedings, Part I 18. Springer, pp 612\u2013629","DOI":"10.1007\/978-3-030-30793-6_35"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10276-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-10276-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10276-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,28]],"date-time":"2024-09-28T08:07:30Z","timestamp":1727510850000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-10276-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,12]]},"references-count":52,"journal-issue":{"issue":"32","published-print":{"date-parts":[[2024,11]]}},"alternative-id":["10276"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-10276-1","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2024,8,12]]},"assertion":[{"value":"9 December 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 July 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 August 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declaration"}},{"value":"The authors have no relevant financial or non-financial interests to disclose. The authors did not receive support from any organization for the submitted work.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The dataset used is partially open source and can be accessed on .","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval and informed consent"}}]}}