{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,29]],"date-time":"2025-05-29T04:07:47Z","timestamp":1748491667343,"version":"3.41.0"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2025,5,28]],"date-time":"2025-05-28T00:00:00Z","timestamp":1748390400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,5,28]],"date-time":"2025-05-28T00:00:00Z","timestamp":1748390400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"The key technologies for major risk prevention, control, and safety assurance of the China Russia pipeline","award":["2022YFC30701"],"award-info":[{"award-number":["2022YFC30701"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"DOI":"10.1007\/s11227-025-07424-2","type":"journal-article","created":{"date-parts":[[2025,5,28]],"date-time":"2025-05-28T15:32:44Z","timestamp":1748446364000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing knowledge graph density through graph relation attention and contrastive learning"],"prefix":"10.1007","volume":"81","author":[{"given":"Chunyu","family":"Lu","sequence":"first","affiliation":[]},{"given":"Tianran","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Duo","family":"Shang","sequence":"additional","affiliation":[]},{"given":"Jun","family":"Luo","sequence":"additional","affiliation":[]},{"given":"Xin","family":"Hui","sequence":"additional","affiliation":[]},{"given":"Ruhui","family":"Shi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,5,28]]},"reference":[{"issue":"7","key":"7424_CR1","first-page":"6418","volume":"35","author":"Y Liu","year":"2021","unstructured":"Liu Y, Wan Y, He L et al (2021) Kg-bart: knowledge graph-augmented bart for generative commonsense reasoning. Proc AAAI Conf Artif Intell 35(7):6418\u20136425","journal-title":"Proc AAAI Conf Artif Intell"},{"issue":"1","key":"7424_CR2","first-page":"78","volume":"15","author":"HO Eke","year":"2020","unstructured":"Eke HO, Ogbuji CN (2020) Transparent stakeholder involvement and corporate credibility of oil firms in Nigeria. Niger Acad Manag J 15(1):78\u201397","journal-title":"Niger Acad Manag J"},{"key":"7424_CR3","doi-asserted-by":"publisher","first-page":"3113","DOI":"10.1109\/TMM.2022.3155900","volume":"25","author":"Y Wu","year":"2022","unstructured":"Wu Y, Liao L, Zhang G et al (2022) State graph reasoning for multimodal conversational recommendation. IEEE Trans Multimedia 25:3113\u20133124","journal-title":"IEEE Trans Multimedia"},{"key":"7424_CR4","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1016\/j.sbi.2021.09.003","volume":"72","author":"X Zeng","year":"2022","unstructured":"Zeng X, Tu X, Liu Y et al (2022) Toward better drug discovery with knowledge graph. Curr Opin Struct Biol 72:114\u2013126","journal-title":"Curr Opin Struct Biol"},{"key":"7424_CR5","doi-asserted-by":"crossref","unstructured":"Yang Y, Huang C, Xia L et al (2022) Knowledge graph contrastive learning for recommendation. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp 1434\u20131443","DOI":"10.1145\/3477495.3532009"},{"issue":"82","key":"7424_CR6","first-page":"1","volume":"22","author":"M Ali","year":"2021","unstructured":"Ali M, Berrendorf M, Hoyt CT et al (2021) PyKEEN 1.0: a python library for training and evaluating knowledge graph embeddings. J Mach Learn Res 22(82):1\u20136","journal-title":"J Mach Learn Res"},{"issue":"2","key":"7424_CR7","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1109\/TBDATA.2022.3177455","volume":"9","author":"X Wang","year":"2022","unstructured":"Wang X, Bo D, Shi C et al (2022) A survey on heterogeneous graph embedding: methods, techniques, applications and sources. IEEE Trans Big Data 9(2):415\u2013436","journal-title":"IEEE Trans Big Data"},{"key":"7424_CR8","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1016\/j.neucom.2022.01.037","volume":"480","author":"S Yao","year":"2022","unstructured":"Yao S, Pi D, Chen J (2022) Knowledge embedding via hyperbolic skipped graph convolutional networks. Neurocomputing 480:119\u2013130","journal-title":"Neurocomputing"},{"key":"7424_CR9","doi-asserted-by":"crossref","unstructured":"Bansal T, Juan D C, Ravi S et al (2019) A2N: Attending to neighbors for knowledge graph inference. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp 4387\u20134392","DOI":"10.18653\/v1\/P19-1431"},{"key":"7424_CR10","doi-asserted-by":"publisher","first-page":"10553","DOI":"10.1007\/s10586-024-04514-3","volume":"27","author":"X Zhao","year":"2024","unstructured":"Zhao X, Yang M, Yang H (2024) A knowledge graph embedding model based on multi-level analogical reasoning. Clust Comput 27:10553\u201310567","journal-title":"Clust Comput"},{"key":"7424_CR11","doi-asserted-by":"publisher","first-page":"120380","DOI":"10.1016\/j.eswa.2023.120380","volume":"228","author":"H Yin","year":"2023","unstructured":"Yin H, Zhong J, Wang C et al (2023) GS-InGAT: an interaction graph attention network with global semantic for knowledge graph completion. Expert Syst Appl 228:120380","journal-title":"Expert Syst Appl"},{"key":"7424_CR12","doi-asserted-by":"crossref","unstructured":"Li Y, Yu K, Huang X et al (2022) Learning inter-entity-interaction for few-shot knowledge graph completion. In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pp 7691\u20137700","DOI":"10.18653\/v1\/2022.emnlp-main.524"},{"key":"7424_CR13","doi-asserted-by":"publisher","first-page":"106265","DOI":"10.1016\/j.neunet.2024.106265","volume":"174","author":"C Liu","year":"2024","unstructured":"Liu C, Zhan Y, Ma X et al (2024) Exploring sparsity in graph transformers. Neural Netw 174:106265","journal-title":"Neural Netw"},{"key":"7424_CR14","doi-asserted-by":"crossref","unstructured":"Dettmers T, Minervini P, Stenetorp P et al (2018) Convolutional 2D knowledge graph embeddings. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 32, no 1","DOI":"10.1609\/aaai.v32i1.11573"},{"key":"7424_CR15","doi-asserted-by":"publisher","first-page":"108235","DOI":"10.1016\/j.knosys.2022.108235","volume":"241","author":"H Liu","year":"2022","unstructured":"Liu H, Zhou S, Chen C et al (2022) Dynamic knowledge graph reasoning based on deep reinforcement learning. Knowl-Based Syst 241:108235","journal-title":"Knowl-Based Syst"},{"key":"7424_CR16","doi-asserted-by":"crossref","unstructured":"Pillai SG, Soon LK, Haw SC (2019) Comparing DBpedia, Wikidata, and YAGO for web information retrieval. In: Intelligent and Interactive Computing: Proceedings of IIC 2018. Springer, Singapore, pp 525\u2013535","DOI":"10.1007\/978-981-13-6031-2_40"},{"issue":"5","key":"7424_CR17","first-page":"4608","volume":"35","author":"Y Zhao","year":"2022","unstructured":"Zhao Y, Zhou H, Zhang A et al (2022) Connecting embeddings based on multiplex relational graph attention networks for knowledge graph entity typing. IEEE Trans Knowl Data Eng 35(5):4608\u20134620","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"4","key":"7424_CR18","first-page":"4801","volume":"37","author":"Z Yao","year":"2023","unstructured":"Yao Z, Zhang W, Chen M et al (2023) Analogical inference enhanced knowledge graph embedding. Proc AAAI Conf Artif Intell 37(4):4801\u20134808","journal-title":"Proc AAAI Conf Artif Intell"},{"issue":"5","key":"7424_CR19","first-page":"4486","volume":"35","author":"L Xia","year":"2021","unstructured":"Xia L, Huang C, Xu Y et al (2021) Knowledge-enhanced hierarchical graph transformer network for multi-behavior recommendation. Proc AAAI Conf Artif Intell 35(5):4486\u20134493","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"7424_CR20","doi-asserted-by":"crossref","unstructured":"Tang M, Su C, Chen H et al (2020) SALKG: a semantic annotation system for building a high-quality legal knowledge graph. In: 2020 IEEE International Conference on Big Data (Big Data). IEEE, pp 2153\u20132159","DOI":"10.1109\/BigData50022.2020.9378107"},{"issue":"3","key":"7424_CR21","doi-asserted-by":"publisher","first-page":"103642","DOI":"10.1016\/j.ipm.2024.103642","volume":"61","author":"W Deng","year":"2024","unstructured":"Deng W, Zhang Y, Yu H et al (2024) Knowledge graph embedding based on dynamic adaptive atrous convolution and attention mechanism for link prediction. Inf Process Manag 61(3):103642","journal-title":"Inf Process Manag"},{"key":"7424_CR22","doi-asserted-by":"crossref","unstructured":"Xiong B, Zhu S, Nayyeri M et al (2022) Ultrahyperbolic knowledge graph embeddings. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp 2130\u20132139","DOI":"10.1145\/3534678.3539333"},{"issue":"2","key":"7424_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3372117","volume":"11","author":"S Zhang","year":"2020","unstructured":"Zhang S, Balog K (2020) Web table extraction, retrieval, and augmentation: a survey. ACM Trans Intell Syst Technol (TIST) 11(2):1\u201335","journal-title":"ACM Trans Intell Syst Technol (TIST)"},{"key":"7424_CR24","doi-asserted-by":"publisher","first-page":"121446","DOI":"10.1016\/j.eswa.2023.121446","volume":"237","author":"D Jiang","year":"2024","unstructured":"Jiang D, Wang R, Xue L et al (2024) Multisource hierarchical neural network for knowledge graph embedding. Expert Syst Appl 237:121446","journal-title":"Expert Syst Appl"},{"issue":"4","key":"7424_CR25","doi-asserted-by":"publisher","first-page":"589","DOI":"10.1007\/s13735-022-00256-3","volume":"11","author":"X Zhang","year":"2022","unstructured":"Zhang X, Fang Q, Hu J et al (2022) TCKGE: transformers with contrastive learning for knowledge graph embedding. Int J Multimed Inf Retr 11(4):589\u2013597","journal-title":"Int J Multimed Inf Retr"},{"key":"7424_CR26","doi-asserted-by":"publisher","first-page":"127044","DOI":"10.1016\/j.neucom.2023.127044","volume":"566","author":"Z Bi","year":"2024","unstructured":"Bi Z, Cheng S, Chen J et al (2024) Relphormer: relational graph transformer for knowledge graph representations. Neurocomputing 566:127044","journal-title":"Neurocomputing"},{"key":"7424_CR27","doi-asserted-by":"crossref","unstructured":"Chung C, Lee J, Whang JJ (2023) Representation learning on hyper-relational and numeric knowledge graphs with transformers. In: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp 310\u2013322","DOI":"10.1145\/3580305.3599490"},{"issue":"7","key":"7424_CR28","doi-asserted-by":"publisher","first-page":"101610","DOI":"10.1016\/j.jksuci.2023.101610","volume":"35","author":"A Onan","year":"2023","unstructured":"Onan A (2023) Hierarchical graph-based text classification framework with contextual node embedding and BERT-based dynamic fusion. J King Saud Univ Comput Inf Sci 35(7):101610","journal-title":"J King Saud Univ Comput Inf Sci"},{"key":"7424_CR29","doi-asserted-by":"publisher","first-page":"406","DOI":"10.1109\/TASLP.2020.3040507","volume":"29","author":"W Song","year":"2020","unstructured":"Song W, Guo J, Fu R et al (2020) A knowledge graph embedding approach for metaphor processing. IEEE\/ACM Trans Audio Speech Lang Process 29:406\u2013420","journal-title":"IEEE\/ACM Trans Audio Speech Lang Process"},{"key":"7424_CR30","first-page":"21171","volume":"35","author":"Z Zhang","year":"2022","unstructured":"Zhang Z, Liu Q, Hu Q et al (2022) Hierarchical graph transformer with adaptive node sampling. Adv Neural Inf Process Syst 35:21171\u201321183","journal-title":"Adv Neural Inf Process Syst"},{"key":"7424_CR31","first-page":"162","volume":"2022","author":"X Xie","year":"2022","unstructured":"Xie X, Zhang N, Li Z et al (2022) From discrimination to generation: knowledge graph completion with generative transformer. Companion Proc Web Conf 2022:162\u2013165","journal-title":"Companion Proc Web Conf"},{"key":"7424_CR32","doi-asserted-by":"crossref","unstructured":"Liu Y, Sun Z, Li G et al (2022) I know what you do not know: Knowledge graph embedding via co-distillation learning. In: Proceedings of the 31st ACM International Conference on Information & Knowledge Management, pp 1329\u20131338","DOI":"10.1145\/3511808.3557355"},{"issue":"7","key":"7424_CR33","doi-asserted-by":"publisher","first-page":"btad410","DOI":"10.1093\/bioinformatics\/btad410","volume":"39","author":"Z Gu","year":"2023","unstructured":"Gu Z, Luo X, Chen J et al (2023) Hierarchical graph transformer with contrastive learning for protein function prediction. Bioinformatics 39(7):btad410","journal-title":"Bioinformatics"},{"key":"7424_CR34","first-page":"2581","volume":"2023","author":"W Zhang","year":"2023","unstructured":"Zhang W, Zhu Y, Chen M et al (2023) Structure pretraining and prompt tuning for knowledge graph transfer. Proc ACM Web Conf 2023:2581\u20132590","journal-title":"Proc ACM Web Conf"},{"issue":"2","key":"7424_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3424672","volume":"15","author":"A Rossi","year":"2021","unstructured":"Rossi A, Barbosa D, Firmani D et al (2021) Knowledge graph embedding for link prediction: a comparative analysis. ACM Trans Knowl Discov Data (TKDD) 15(2):1\u201349","journal-title":"ACM Trans Knowl Discov Data (TKDD)"},{"issue":"10","key":"7424_CR36","first-page":"11112","volume":"36","author":"J Ni","year":"2022","unstructured":"Ni J, Pandelea V, Young T et al (2022) Hitkg: towards goal-oriented conversations via multi-hierarchy learning. Proc AAAI Conf Artif Intell 36(10):11112\u201311120","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"7424_CR37","first-page":"2519","volume":"2023","author":"J Dong","year":"2023","unstructured":"Dong J, Zhang Q, Huang X et al (2023) Hierarchy-aware multi-hop question answering over knowledge graphs. Proc ACM Web Conf 2023:2519\u20132527","journal-title":"Proc ACM Web Conf"},{"issue":"2","key":"7424_CR38","doi-asserted-by":"publisher","first-page":"494","DOI":"10.1109\/TNNLS.2021.3070843","volume":"33","author":"S Ji","year":"2021","unstructured":"Ji S, Pan S, Cambria E et al (2021) A survey on knowledge graphs: representation, acquisition, and applications. IEEE Trans Neural Netw Learn Syst 33(2):494\u2013514","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"7424_CR39","unstructured":"Yang J, Ying X, Shi Y et al (2022) Learning hierarchy-aware quaternion knowledge graph embeddings with representing relations as 3D rotations. In: Proceedings of the 29th International Conference on Computational Linguistics, pp 2011\u20132023"},{"issue":"8","key":"7424_CR40","first-page":"7151","volume":"35","author":"Z Cui","year":"2021","unstructured":"Cui Z, Kapanipathi P, Talamadupula K et al (2021) Type-augmented relation prediction in knowledge graphs. Proc AAAI Conf Artif Intell 35(8):7151\u20137159","journal-title":"Proc AAAI Conf Artif Intell"},{"issue":"7","key":"7424_CR41","first-page":"6271","volume":"35","author":"J Chen","year":"2021","unstructured":"Chen J, He H, Wu F et al (2021) Topology-aware correlations between relations for inductive link prediction in knowledge graphs. Proc AAAI Conf Artif Intell 35(7):6271\u20136278","journal-title":"Proc AAAI Conf Artif Intell"},{"issue":"6","key":"7424_CR42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3643806","volume":"56","author":"J Cao","year":"2024","unstructured":"Cao J, Fang J, Meng Z et al (2024) Knowledge graph embedding: a survey from the perspective of representation spaces. ACM Comput Surv 56(6):1\u201342","journal-title":"ACM Comput Surv"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-07424-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-025-07424-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-07424-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,28]],"date-time":"2025-05-28T15:32:51Z","timestamp":1748446371000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-025-07424-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,28]]},"references-count":42,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["7424"],"URL":"https:\/\/doi.org\/10.1007\/s11227-025-07424-2","relation":{},"ISSN":["1573-0484"],"issn-type":[{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,28]]},"assertion":[{"value":"9 May 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 May 2025","order":2,"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 conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"912"}}