{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T11:49:39Z","timestamp":1773229779339,"version":"3.50.1"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T00:00:00Z","timestamp":1768435200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T00:00:00Z","timestamp":1771200000000},"content-version":"vor","delay-in-days":32,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"the Doctoral Research Start-up Fund of Zhengzhou University of Light Industry","award":["2024BSJJ030"],"award-info":[{"award-number":["2024BSJJ030"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J. King Saud Univ. Comput. Inf. Sci."],"published-print":{"date-parts":[[2026,3]]},"DOI":"10.1007\/s44443-025-00461-0","type":"journal-article","created":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T15:34:54Z","timestamp":1768491294000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["QLGAN: a quantum-lineage graph attention network for temporal knowledge graph entity alignment"],"prefix":"10.1007","volume":"38","author":[{"given":"Jia","family":"Li","sequence":"first","affiliation":[]},{"given":"Yuxi","family":"Ma","sequence":"additional","affiliation":[]},{"given":"Lingzhong","family":"Meng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,15]]},"reference":[{"issue":"1","key":"461_CR1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevA.108.012410","volume":"108","author":"K Beer","year":"2023","unstructured":"Beer K, Khosla M, K\u00f6hler J, Osborne TJ, Zhao T (2023) Quantum machine learning of graph-structured data. Phys Rev A 108(1):012410","journal-title":"Phys Rev A"},{"issue":"7671","key":"461_CR2","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1038\/nature23474","volume":"549","author":"J Biamonte","year":"2017","unstructured":"Biamonte J, Wittek P, Pancotti N, Rebentrost P, Wiebe N, Lloyd S (2017) Quantum machine learning. Nature 549(7671):195\u2013202","journal-title":"Nature"},{"key":"461_CR3","unstructured":"Cai L, Mao X, Ma M, Yuan H, Zhu J, Lan M (2022) A simple temporal information matching mechanism for entity alignment between temporal knowledge graphs. In: Proceedings of the 29th international conference on computational linguistics, pp 2075\u20132086"},{"key":"461_CR4","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. The 57th Annual Meeting of the Association for Computational Linguistics (ACL)","DOI":"10.18653\/v1\/P19-1140"},{"key":"461_CR5","doi-asserted-by":"crossref","unstructured":"Chen L, Li Z, Xu T, Wu H, Wang Z, Yuan NJ, Chen E (2022) Multi-modal siamese network for entity alignment. In: Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining, pp 118\u2013126","DOI":"10.1145\/3534678.3539244"},{"key":"461_CR6","doi-asserted-by":"crossref","unstructured":"Chen M, Tian Y, Yang M, Zaniolo C (2017) Multilingual knowledge graph embeddings for cross-lingual knowledge alignment. the 26th International Joint Conference on Artificial Intelligence(IJCAI)","DOI":"10.24963\/ijcai.2017\/209"},{"key":"461_CR7","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"},{"issue":"1","key":"461_CR8","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1007\/s42484-024-00143-6","volume":"6","author":"N Innan","year":"2024","unstructured":"Innan N, Sawaika A, Dhor A, Dutta S, Thota S, Gokal H, Patel N, Khan MA-Z, Theodonis I, Bennai M (2024) Financial fraud detection using quantum graph neural networks. Quantum Mach Intell 6(1):7","journal-title":"Quantum Mach Intell"},{"key":"461_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.datak.2024.102300","volume":"151","author":"W Jia","year":"2024","unstructured":"Jia W, Ma R, Yan L, Niu W, Ma Z (2024) Time-aware structure matching for temporal knowledge graph alignment. Data & Knowl Eng 151:102300","journal-title":"Data & Knowl Eng"},{"key":"461_CR10","doi-asserted-by":"crossref","unstructured":"Jiang X, Shen Y, Shi Z, Xu C, Li W, Li Z, Guo J, Shen H, Wang Y (2024) Unlocking the power of large language models for entity alignment. In: Proceedings of the 62nd annual meeting of the association for computational linguistics (Volume 1: Long Papers), pp 7566\u20137583","DOI":"10.18653\/v1\/2024.acl-long.408"},{"key":"461_CR11","doi-asserted-by":"crossref","unstructured":"Jiang X, Shen Y, Shi Z, Xu C, Li W, Zihe H, Guo J, Wang Y (2024) Mm-chatalign: A novel multimodal reasoning framework based on large language models for entity alignment. In: Findings of the association for computational linguistics EMNLP 2024, pp 2637\u20132654","DOI":"10.18653\/v1\/2024.findings-emnlp.148"},{"key":"461_CR12","unstructured":"Knight PA (2007) The sinkhorn\u2013knopp algorithm: Convergence and applications. In: Web information retrieval and linear algebra algorithms, 11.02. - 16.02.2007"},{"issue":"2","key":"461_CR13","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1007\/s42484-025-00305-0","volume":"7","author":"C-Y Liu","year":"2025","unstructured":"Liu C-Y, Kuo E-J, Abraham Lin C-H, Gemsun Young J, Chang Y-J, Hsieh M-H, Goan H-S (2025) Quantum-train: Rethinking hybrid quantum-classical machine learning in the model compression perspective. Quantum Mach Intell 7(2):80","journal-title":"Quantum Mach Intell"},{"key":"461_CR14","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. In EMNLP","DOI":"10.18653\/v1\/2020.emnlp-main.515"},{"key":"461_CR15","doi-asserted-by":"crossref","unstructured":"Liu F, Chen M, Roth D, Collier N (2021) Visual pivoting for (unsupervised) entity alignment. In: AAAI, 2021","DOI":"10.1609\/aaai.v35i5.16550"},{"key":"461_CR16","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"},{"key":"461_CR17","doi-asserted-by":"crossref","unstructured":"Mao X, Wang W, Wu Y, Lan M (2021) Boosting the speed of entity alignment 10*: Dual attention matching network with normalized hard sample mining. In WWW,2021","DOI":"10.1145\/3442381.3449897"},{"key":"461_CR18","doi-asserted-by":"crossref","unstructured":"Mao X, Wang W, Wu Y, Lan M (2022) Lightea: A scalable, robust, and interpretable entity alignment framework via three-view label propagation. In: Proceedings of the 2022 conference on empirical methods in natural language processing, pp 825\u2013838","DOI":"10.18653\/v1\/2022.emnlp-main.52"},{"key":"461_CR19","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":"461_CR20","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":"461_CR21","doi-asserted-by":"crossref","unstructured":"OZHIGOV Y (2022) Three principles of quantum computing. Quantum Inf Comput 22(15&16):1280\u20131288","DOI":"10.26421\/QIC22.15-16-2"},{"issue":"4","key":"461_CR22","doi-asserted-by":"publisher","first-page":"410","DOI":"10.3390\/math9040410","volume":"9","author":"D Pomarico","year":"2021","unstructured":"Pomarico D, Fanizzi A, Amoroso N, Bellotti R, Biafora A, Bove S, Didonna V, Forgia DL, Pastena MI, Tamborra P et al (2021) A proposal of quantum-inspired machine learning for medical purposes: An application case. Mathematics 9(4):410","journal-title":"Mathematics"},{"key":"461_CR23","doi-asserted-by":"crossref","unstructured":"Qi H, Wang L, Gong C, Gani A (2024) A survey on quantum data mining algorithms: challenges, advances and future directions. Quantum Information Processing 23(3)","DOI":"10.1007\/s11128-024-04279-z"},{"issue":"1","key":"461_CR24","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1140\/epjqt\/s40507-024-00285-3","volume":"11","author":"M Rath","year":"2024","unstructured":"Rath M, Date H (2024) Quantum data encoding: A comparative analysis of classical-to-quantum mapping techniques and their impact on machine learning accuracy. EPJ Quantum Technology 11(1):72","journal-title":"EPJ Quantum Technology"},{"key":"461_CR25","doi-asserted-by":"crossref","unstructured":"Ray A, Madan D, Patil S, Pati P, Rapsomaniki M, Kohlakala A, Dlamini TR, Muller SJ, Rhrissorrakrai K, Utro F, et al (2024) Hybrid quantum-classical graph neural networks for tumor classification in digital pathology. In: 2024 IEEE international conference on quantum computing and engineering (QCE), vol 1, pp 1611\u20131616 . IEEE","DOI":"10.1109\/QCE60285.2024.00188"},{"key":"461_CR26","doi-asserted-by":"crossref","unstructured":"Sun Z, Chen M, Hu W, Wang C, Dai J, Zhang W (2020) Knowledge association with hyperbolic knowledge graph embeddings. In EMNLP","DOI":"10.18653\/v1\/2020.emnlp-main.460"},{"key":"461_CR27","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, pp 222\u2013229","DOI":"10.1609\/aaai.v34i01.5354"},{"key":"461_CR28","doi-asserted-by":"crossref","unstructured":"Trsedya BD, Qi J, Rui Z (2019) Entity alignment between knowledge graphs using attribute embeddings. Proceedings of the AAAI Conference On Artificial Intelligence 33:297\u2013304","DOI":"10.1609\/aaai.v33i01.3301297"},{"issue":"2","key":"461_CR29","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1007\/s42484-021-00055-9","volume":"3","author":"C T\u00fcys\u00fcz","year":"2021","unstructured":"T\u00fcys\u00fcz C, Rieger C, Novotny K, Demirk\u00f6z B, Dobos D, Potamianos K, Vallecorsa S, Vlimant J-R, Forster R (2021) Hybrid quantum classical graph neural networks for particle track reconstruction. Quantum Mach Intell 3(2):29","journal-title":"Quantum Mach Intell"},{"key":"461_CR30","unstructured":"Verdon G, Mccourt T, Luzhnica E, Singh V, Leichenauer S, Hidary J (2019) Quantum graph neural networks"},{"key":"461_CR31","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":"461_CR32","doi-asserted-by":"crossref","unstructured":"Xu Y, Huang H, State R (2024) Ctqw-graphsage: Trainabel continuous-time quantum walk on graph. In: International conference on artificial neural networks, pp 79\u201392 . Springer","DOI":"10.1007\/978-3-031-72344-5_6"},{"key":"461_CR33","doi-asserted-by":"crossref","unstructured":"Xu C, Su F, et al (2021) Time-aware graph neural network for entity alignment between temporal knowledge graphs. In: EMNLP","DOI":"10.18653\/v1\/2021.emnlp-main.709"},{"key":"461_CR34","doi-asserted-by":"crossref","unstructured":"Xu C, Su F, Xiong B, Lehmann J (2022) Time-aware entity alignment using temporal relational attention. In: Proceedings of the ACM web conference 2022, pp 788\u2013797","DOI":"10.1145\/3485447.3511922"},{"key":"461_CR35","doi-asserted-by":"crossref","unstructured":"Yang H-W, Zou Y, Shi P, Lu W, Lin J, Sun X (2020) Aligning cross-lingual entities with multi-aspect information. In: Proceedings of the 2020 conference on empirical methods in natural language processing (EMNLP)","DOI":"10.18653\/v1\/D19-1451"},{"key":"461_CR36","doi-asserted-by":"crossref","unstructured":"Yu D, Yang Y, Zhang R, Wu Y (2021) Knowledge embedding based graph convolutional network. In: Proceedings of the web conference 2021, pp 1619\u20131628","DOI":"10.1145\/3442381.3449925"},{"key":"461_CR37","doi-asserted-by":"crossref","unstructured":"Zhu H, Xie R, Liu Z, Sun M (2017) Iterative entity alignment via joint knowledge embeddings. In: IJCAI, vol 17, pp 4258\u20134264","DOI":"10.24963\/ijcai.2017\/595"}],"container-title":["Journal of King Saud University Computer and Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44443-025-00461-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44443-025-00461-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44443-025-00461-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T14:40:10Z","timestamp":1773153610000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44443-025-00461-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,15]]},"references-count":37,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,3]]}},"alternative-id":["461"],"URL":"https:\/\/doi.org\/10.1007\/s44443-025-00461-0","relation":{},"ISSN":["1319-1578","2213-1248"],"issn-type":[{"value":"1319-1578","type":"print"},{"value":"2213-1248","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,15]]},"assertion":[{"value":"28 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 December 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 January 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"66"}}