{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T19:01:37Z","timestamp":1772910097997,"version":"3.50.1"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2023,9,15]],"date-time":"2023-09-15T00:00:00Z","timestamp":1694736000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,9,15]],"date-time":"2023-09-15T00:00:00Z","timestamp":1694736000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-023-16670-6","type":"journal-article","created":{"date-parts":[[2023,9,15]],"date-time":"2023-09-15T05:02:11Z","timestamp":1694754131000},"page":"34269-34289","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Entity alignment based on informative neighbor sampling and multi-embedding graph matching"],"prefix":"10.1007","volume":"83","author":[{"given":"Chunmei","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongbin","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhijun","family":"Fang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,15]]},"reference":[{"issue":"3","key":"16670_CR1","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1016\/j.websem.2009.07.002","volume":"7","author":"C Bizer","year":"2009","unstructured":"Bizer C, Lehmann J, Kobilarov G, Auer S, Becker C, Cyganiak R, Hellmann S (2009) Dbpedia - a crystallization point for the web of data. Web Semant 7(3):154\u2013165. https:\/\/doi.org\/10.1016\/j.websem.2009.07.002","journal-title":"Web Semant"},{"issue":"3","key":"16670_CR2","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1016\/j.websem.2008.06.001","volume":"6","author":"FM Suchanek","year":"2008","unstructured":"Suchanek FM, Kasneci G, Weikum G (2008) Yago: A large ontology from wikipedia and wordnet. Web Semant 6(3):203\u2013217. https:\/\/doi.org\/10.1016\/j.websem.2008.06.001","journal-title":"Web Semant"},{"key":"16670_CR3","doi-asserted-by":"publisher","unstructured":"Bollacker K (2008) Freebase : A collaboratively created graph database for structuring human knowledge. Proc. SIGMOD\u2019 08. https:\/\/doi.org\/10.1145\/1376616.1376746","DOI":"10.1145\/1376616.1376746"},{"key":"16670_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.websem.2018.07.001","volume":"51","author":"D Moussallem","year":"2018","unstructured":"Moussallem D, Wauer M, Ngomo A-CN (2018) Machine translation using semantic web technologies: A survey. J Web Semant 51:1\u201319. https:\/\/doi.org\/10.1016\/j.websem.2018.07.001","journal-title":"J Web Semant"},{"key":"16670_CR5","doi-asserted-by":"publisher","unstructured":"Huang X, Zhang J, Li D, Li P (2019) Knowledge graph embedding based question answering. In: Proceedings of the twelfth acm international conference on web search and data mining. WSDM \u201919, Association for Computing Machinery, New York, NY, USA, pp 105\u2013113. https:\/\/doi.org\/10.1145\/3289600.3290956","DOI":"10.1145\/3289600.3290956"},{"key":"16670_CR6","doi-asserted-by":"publisher","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. In: Proceedings of the twenty-seventh international joint conference on artificial intelligence, IJCAI-18, pp 3998\u20134004. https:\/\/doi.org\/10.24963\/ijcai.2018\/556","DOI":"10.24963\/ijcai.2018\/556"},{"key":"16670_CR7","doi-asserted-by":"publisher","unstructured":"Spohr D, Hollink L , Cimiano P (2011) A machine learning approach to multilingual and cross-lingual ontology matching. In: Proceedings of the 10th international conference on the semantic web - volume part I. ISWC\u201911, Springer, Berlin, Heidelberg, pp 665\u2013680. https:\/\/doi.org\/10.5555\/2063016.2063059","DOI":"10.5555\/2063016.2063059"},{"key":"16670_CR8","unstructured":"Bordes A, Usunier N, Garcia-Duran A, Weston J, Yakhnenko O (2013) Translating embeddings for modeling multi-relational data. In: Burges CJC, Bottou L, Welling M, Ghahramani Z, Weinberger KQ (eds) Advances in neural information processing systems, vol 26. https:\/\/proceedings.neurips.cc\/paper\/2013\/file\/1cecc7a77928ca8133fa24680a88d2f9-Paper.pdf"},{"key":"16670_CR9","doi-asserted-by":"publisher","unstructured":"Sun Z, Hu W, Li C (2017) Cross-lingual entity alignment via joint attribute-preserving embedding, pp 628\u2013644. https:\/\/doi.org\/10.1007\/978-3-319-68288-4_37","DOI":"10.1007\/978-3-319-68288-4_37"},{"key":"16670_CR10","doi-asserted-by":"publisher","unstructured":"Chen M, Tian Y, Yang M, Zaniolo C (2017) Multilingual knowledge graph embeddings for cross-lingual knowledge alignment. In: Proceedings of the 26th international joint conference on artificial intelligence. IJCAI\u201917, AAAI Press, Melbourne, Australia, pp 1511\u20131517. https:\/\/doi.org\/10.5555\/3172077.3172097","DOI":"10.5555\/3172077.3172097"},{"key":"16670_CR11","doi-asserted-by":"publisher","unstructured":"Zhu H, Xie R, Liu Z, Sun M (2017) Iterative entity alignment via joint knowledge embeddings. In: Proceedings of the twenty-sixth international joint conference on artificial intelligence, IJCAI-17, pp 4258\u20134264. https:\/\/doi.org\/10.24963\/ijcai.2017\/595","DOI":"10.24963\/ijcai.2017\/595"},{"key":"16670_CR12","doi-asserted-by":"publisher","unstructured":"Sun Z, Hu W, Zhang Q, Qu Y (2018) Bootstrapping entity alignment with knowledge graph embedding. In: Proceedings of the twenty-seventh international joint conference on artificial intelligence, IJCAI-18, pp 4396\u20134402. https:\/\/doi.org\/10.24963\/ijcai.2018\/611","DOI":"10.24963\/ijcai.2018\/611"},{"key":"16670_CR13","doi-asserted-by":"publisher","unstructured":"Ying R, He R, Chen K, Eksombatchai P, Hamilton WL, Leskovec J (2018) Graph Convolutional Neural Networks for Web-Scale Recommender Systems. ACM. https:\/\/doi.org\/10.1145\/3219819.3219890","DOI":"10.1145\/3219819.3219890"},{"key":"16670_CR14","doi-asserted-by":"publisher","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. Association for Computational Linguistics, Brussels, Belgium, pp 349\u2013357. https:\/\/doi.org\/10.18653\/v1\/D18-1032","DOI":"10.18653\/v1\/D18-1032"},{"key":"16670_CR15","doi-asserted-by":"publisher","unstructured":"Wu Y, Liu X, Feng Y, Wang Z, Yan R, Zhao D (2019) Relation-aware entity alignment for heterogeneous knowledge graphs. In: Proceedings of the twenty-eighth international joint conference on artificial intelligence, IJCAI-19, pp 5278\u20135284. https:\/\/doi.org\/10.24963\/ijcai.2019\/733","DOI":"10.24963\/ijcai.2019\/733"},{"key":"16670_CR16","doi-asserted-by":"publisher","unstructured":"Ye R, Li X, Fang Y, Zang H, Wang M (2019) A vectorized relational graph convolutional network for multi-relational network alignment. In: Twenty-eighth international joint conference on artificial intelligence IJCAI-19. https:\/\/doi.org\/10.24963\/ijcai.2019\/574","DOI":"10.24963\/ijcai.2019\/574"},{"issue":"1","key":"16670_CR17","doi-asserted-by":"publisher","first-page":"222","DOI":"10.1609\/aaai.v34i01.5354","volume":"34","author":"Z Sun","year":"2020","unstructured":"Sun Z, Wang C, Hu W, Chen M, Qu Y (2020) Knowledge graph alignment network with gated multi-hop neighborhood aggregation. Proc AAAI Con Art Intell 34(1):222\u2013229. https:\/\/doi.org\/10.1609\/aaai.v34i01.5354","journal-title":"Proc AAAI Con Art Intell"},{"key":"16670_CR18","doi-asserted-by":"publisher","unstructured":"Mao X, Wang W, Wu Y, Lan M (2021) From alignment to assignment: Frustratingly simple unsupervised entity alignment. In: Proceedings of the 2021 conference on empirical methods in natural language processing. Association for Computational Linguistics, Online and Punta Cana, Dominican Republic, pp 2843\u20132853. https:\/\/doi.org\/10.18653\/v1\/2021.emnlp-main.226","DOI":"10.18653\/v1\/2021.emnlp-main.226"},{"key":"16670_CR19","doi-asserted-by":"publisher","unstructured":"Xiang Y, Zhang Z, Chen J, Chen X, Zheng Y (2021) Ontoea: Ontology-guided entity alignment via joint knowledge graph embedding. https:\/\/doi.org\/10.18653\/v1\/2021.findings-acl.96","DOI":"10.18653\/v1\/2021.findings-acl.96"},{"key":"16670_CR20","doi-asserted-by":"publisher","unstructured":"Cai W, Ma W, Zhan J, Jiang Y (2022) Entity alignment with reliable path reasoning and relation-aware heterogeneous graph transformer. In: Raedt LD (ed) Proceedings of the thirty-first international joint conference on artificial intelligence, IJCAI-22. International Joint Conferences on Artificial Intelligence Organization,???, pp 1930\u20131937. Main Track. https:\/\/doi.org\/10.24963\/ijcai.2022\/268","DOI":"10.24963\/ijcai.2022\/268"},{"key":"16670_CR21","doi-asserted-by":"publisher","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. In: Proceedings of the 57th annual meeting of the association for computational linguistics. Association for Computational Linguistics, Florence, Italy, pp 3156\u20133161. https:\/\/doi.org\/10.18653\/v1\/P19-1304","DOI":"10.18653\/v1\/P19-1304"},{"key":"16670_CR22","doi-asserted-by":"publisher","unstructured":"Wu Y, Liu X, Feng Y, Wang Z, Zhao D (2020) Neighborhood matching network for entity alignment. In: Proceedings of the 58th annual meeting of the association for computational linguistics. Association for Computational Linguistics, Online. pp 6477\u20136487. https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.578","DOI":"10.18653\/v1\/2020.acl-main.578"},{"key":"16670_CR23","doi-asserted-by":"publisher","unstructured":"Pei S, Yu L, Zhang X (2019) Improving cross-lingual entity alignment via optimal transport. In: Proceedings of the twenty-eighth international joint conference on artificial intelligence, IJCAI-19. International Joint Conferences on Artificial Intelligence Organization, ???, pp 3231\u20133237. https:\/\/doi.org\/10.24963\/ijcai.2019\/448","DOI":"10.24963\/ijcai.2019\/448"},{"key":"16670_CR24","doi-asserted-by":"publisher","unstructured":"Guo L, Sun Z, Hu W (2019) Learning to exploit long-term relational dependencies in knowledge graphs. In: ICML, pp 2505\u20132514. https:\/\/doi.org\/10.48550\/arXiv.1905.04914","DOI":"10.48550\/arXiv.1905.04914"},{"issue":"1","key":"16670_CR25","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","volume":"32","author":"Z Wu","year":"2021","unstructured":"Wu Z, Pan S, Chen F, Long G, Zhang C, Yu PS (2021) A comprehensive survey on graph neural networks. IEEE Trans Neural Netw Learn Sys 32(1):4\u201324. https:\/\/doi.org\/10.1109\/TNNLS.2020.2978386","journal-title":"IEEE Trans Neural Netw Learn Sys"},{"key":"16670_CR26","doi-asserted-by":"crossref","unstructured":"Marcheggiani D, Titov I (2017) Encoding sentences with graph convolutional networks for semantic role labeling. In: EMNLP, pp 1506\u20131515. https:\/\/aclanthology.info\/papers\/D17-1159\/d17-1159","DOI":"10.18653\/v1\/D17-1159"},{"issue":"01","key":"16670_CR27","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1609\/aaai.v33i01.3301297","volume":"33","author":"BD Trisedya","year":"2019","unstructured":"Trisedya BD, Qi J, Zhang R (2019) Entity alignment between knowledge graphs using attribute embeddings. Proc AAAI Conf Art Intell 33(01):297\u2013304. https:\/\/doi.org\/10.1609\/aaai.v33i01.3301297","journal-title":"Proc AAAI Conf Art Intell"},{"key":"16670_CR28","doi-asserted-by":"publisher","unstructured":"Wu Y, Liu X, Feng Y, Wang Z, Zhao D (2019) Jointly learning entity and relation representations for entity alignment. In: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 240\u2013249. https:\/\/doi.org\/10.18653\/v1\/D19-1023","DOI":"10.18653\/v1\/D19-1023"},{"key":"16670_CR29","doi-asserted-by":"publisher","unstructured":"Chen L, Li Z, Wang Y, Xu T, Wang Z, Chen E (2020) MMEA: Entity alignment for multi-modal knowledge graph, pp 134\u2013147. https:\/\/doi.org\/10.1007\/978-3-030-55130-8_12","DOI":"10.1007\/978-3-030-55130-8_12"},{"key":"16670_CR30","doi-asserted-by":"publisher","unstructured":"Zeng W, Zhao X, Tang J, Lin X (2020) Collective entity alignment via adaptive features. In: 2020 IEEE 36th international conference on data engineering (ICDE), pp 1870\u20131873. https:\/\/doi.org\/10.1109\/ICDE48307.2020.00191","DOI":"10.1109\/ICDE48307.2020.00191"},{"issue":"03","key":"16670_CR31","doi-asserted-by":"publisher","first-page":"3025","DOI":"10.1609\/aaai.v34i03.5696","volume":"34","author":"K Yang","year":"2020","unstructured":"Yang K, Liu S, Zhao J, Wang Y, Xie B (2020) Cotsae: Co-training of structure and attribute embeddings for entity alignment. Proc AAAI Con Art Intell 34(03):3025\u20133032. https:\/\/doi.org\/10.1609\/aaai.v34i03.5696","journal-title":"Proc AAAI Con Art Intell"},{"key":"16670_CR32","doi-asserted-by":"publisher","unstructured":"Zhu Y, Liu H, Wu Z, Du Y (2020) Relation-aware neighborhood matching model for entity alignment. https:\/\/doi.org\/10.48550\/arXiv.2012.08128","DOI":"10.48550\/arXiv.2012.08128"},{"key":"16670_CR33","doi-asserted-by":"publisher","unstructured":"Nguyen TT, Huynh TT, Yin H, Tong VV, Sakong D, Zheng B, Nguyen QVH (2020) Entity alignment for knowledge graphs with multi-order convolutional networks. IEEE Trans Knowl Data Eng 1\u20131. https:\/\/doi.org\/10.1109\/TKDE.2020.3038654","DOI":"10.1109\/TKDE.2020.3038654"},{"key":"16670_CR34","doi-asserted-by":"publisher","unstructured":"Zeng W, Zhao X, Tang J, Lin X, Groth P (2021) Reinforcement learning-based collective entity alignment with adaptive features. ACM Trans Inf Syst 39(3). https:\/\/doi.org\/10.1145\/3446428","DOI":"10.1145\/3446428"},{"key":"16670_CR35","doi-asserted-by":"crossref","unstructured":"Powell M (1992) The theory of radial basis function approximation in 1990. Advances in Numerical Analysis","DOI":"10.1093\/oso\/9780198534396.003.0003"},{"key":"16670_CR36","doi-asserted-by":"publisher","unstructured":"Ge C, Liu X, Chen L, Zheng B, Gao Y (2021) Make it easy: An effective end-to-end entity alignment framework. In: Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval. SIGIR \u201921. Association for Computing Machinery, New York, NY, USA, pp 777\u2013786. https:\/\/doi.org\/10.1145\/3404835.3462870","DOI":"10.1145\/3404835.3462870"},{"key":"16670_CR37","doi-asserted-by":"publisher","unstructured":"Srivastava RK, Greff K, Schmidhuber J (2015) Highway networks. Comput Sci https:\/\/doi.org\/10.48550\/arXiv.1505.00387","DOI":"10.48550\/arXiv.1505.00387"},{"key":"16670_CR38","doi-asserted-by":"publisher","unstructured":"Mudgal S, Li H, Rekatsinas T, Doan A, Park Y, Krishnan G, Deep R, Arcaute E, Raghavendra V (2018) Deep learning for entity matching: A design space exploration. In: Proceedings of the 2018 international conference on management of data. SIGMOD \u201918, Association for Computing Machinery, New York, NY, USA, pp 19\u201334. https:\/\/doi.org\/10.1145\/3183713.3196926","DOI":"10.1145\/3183713.3196926"},{"key":"16670_CR39","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2007.1070","author":"L Yujian","year":"2007","unstructured":"Yujian L, Bo L (2007) A normalized levenshtein distance metric. IEEE Trans Pattern Anal Mac Intell. https:\/\/doi.org\/10.1109\/TPAMI.2007.1070","journal-title":"IEEE Trans Pattern Anal Mac Intell"},{"key":"16670_CR40","doi-asserted-by":"publisher","unstructured":"Cao Y, Liu Z, Li C, Liu Z, Li J, Chua T-S (2019) Multi-channel graph neural network for entity alignment. In: Proceedings of the 57th annual meeting of the association for computational linguistics, Florence, Italy, pp 1452\u20131461. https:\/\/doi.org\/10.18653\/v1\/P19-1140","DOI":"10.18653\/v1\/P19-1140"},{"key":"16670_CR41","doi-asserted-by":"publisher","unstructured":"Chen M, Shi W, Zhou B, Roth D (2021) Cross-lingual Entity Alignment with Incidental Supervision. arXiv https:\/\/doi.org\/10.48550\/ARXIV.2005.00171","DOI":"10.48550\/ARXIV.2005.00171"},{"key":"16670_CR42","doi-asserted-by":"publisher","unstructured":"Joulin A, Grave E, Bojanowski P, Douze M, J\u00e9gou H, Mikolov T (2016) Fasttext.zip: Compressing text classification models. arXiv preprint arXiv:1612.03651https:\/\/doi.org\/10.48550\/arXiv.1612.03651","DOI":"10.48550\/arXiv.1612.03651"},{"key":"16670_CR43","doi-asserted-by":"publisher","unstructured":"Kotnis B, Nastase V (2017) Analysis of the impact of negative sampling on link prediction in knowledge graphs. https:\/\/doi.org\/10.48550\/arXiv.1708.06816","DOI":"10.48550\/arXiv.1708.06816"},{"key":"16670_CR44","doi-asserted-by":"publisher","first-page":"852","DOI":"10.1016\/j.ins.2021.08.042","volume":"577","author":"A Ali","year":"2021","unstructured":"Ali A, Zhu Y, Zakarya M (2021) Exploiting dynamic spatio-temporal correlations for citywide traffic flow prediction using attention based neural networks. Inform Sci 577:852\u2013870. https:\/\/doi.org\/10.1016\/j.ins.2021.08.042","journal-title":"Inform Sci"},{"key":"16670_CR45","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1016\/j.neunet.2021.10.021","volume":"145","author":"A Ali","year":"2022","unstructured":"Ali A, Zhu Y, Zakarya M (2022) Exploiting dynamic spatio-temporal graph convolutional neural networks for citywide traffic flows prediction. Neural Netw 145:233\u2013247. https:\/\/doi.org\/10.1016\/j.neunet.2021.10.021","journal-title":"Neural Netw"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-16670-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-16670-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-16670-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,2]],"date-time":"2024-04-02T13:15:51Z","timestamp":1712063751000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-16670-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,15]]},"references-count":45,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2024,4]]}},"alternative-id":["16670"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-16670-6","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,15]]},"assertion":[{"value":"5 August 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 May 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 August 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 September 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}