{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T07:35:06Z","timestamp":1773300906762,"version":"3.50.1"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"19","license":[{"start":{"date-parts":[[2023,6,24]],"date-time":"2023-06-24T00:00:00Z","timestamp":1687564800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,6,24]],"date-time":"2023-06-24T00:00:00Z","timestamp":1687564800000},"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":["62176236"],"award-info":[{"award-number":["62176236"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62106225"],"award-info":[{"award-number":["62106225"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1007\/s10489-023-04698-y","type":"journal-article","created":{"date-parts":[[2023,6,24]],"date-time":"2023-06-24T12:44:26Z","timestamp":1687610666000},"page":"22132-22142","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Knowledge graph embedding and completion based on entity community and local importance"],"prefix":"10.1007","volume":"53","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9437-262X","authenticated-orcid":false,"given":"Xu-Hua","family":"Yang","sequence":"first","affiliation":[]},{"given":"Gang-Feng","family":"Ma","sequence":"additional","affiliation":[]},{"given":"Xin","family":"Jin","sequence":"additional","affiliation":[]},{"given":"Hai-Xia","family":"Long","sequence":"additional","affiliation":[]},{"given":"Jie","family":"Xiao","sequence":"additional","affiliation":[]},{"given":"Lei","family":"Ye","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,6,24]]},"reference":[{"key":"4698_CR1","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.neunet.2020.08.005","volume":"132","author":"M Yang","year":"2020","unstructured":"Yang M, Chen L, Lyu Z, Liu J, Shen Y, Wu Q (2020) Hierarchical fusion of common sense knowledge and classifier decisions for answer selection in community question answering. Neural Networks 132:53\u201365","journal-title":"Neural Networks"},{"key":"4698_CR2","doi-asserted-by":"crossref","unstructured":"Yang Y, Zhu Y, Li Y (2022) Personalized recommendation with knowledge graph via dual-autoencoder. Appl Intell 52(6):6196\u20136207","DOI":"10.1007\/s10489-021-02647-1"},{"issue":"3","key":"4698_CR3","doi-asserted-by":"publisher","first-page":"663","DOI":"10.1007\/s10489-019-01540-2","volume":"50","author":"AD Vo","year":"2020","unstructured":"Vo AD, Nguyen QP, Ock CY (2020) Semantic and syntactic analysis in learning representation based on a sentiment analysis model. Appl intell 50(3):663\u2013680","journal-title":"Appl intell"},{"key":"4698_CR4","doi-asserted-by":"publisher","first-page":"100159","DOI":"10.1016\/j.bdr.2020.100159","volume":"22","author":"M Wang","year":"2020","unstructured":"Wang M, Wang H, Qi G, Zheng Q (2020) Richpedia: A large-scale, comprehensive multi-modal knowledge graph. Big Data Res 22:100159","journal-title":"Big Data Res"},{"issue":"2","key":"4698_CR5","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, Marttinen P, Philip SY (2021) A survey on knowledge graphs: Representation, acquisition, and applications. IEEE Transactions on Neural Networks and Learning Systems 33(2):494\u2013514","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"4698_CR6","doi-asserted-by":"crossref","unstructured":"Feng J, Wei Q, Cui J, Chen J (2021) Novel translation knowledge graph completion model based on 2d convolution. Appl Intell, 1\u201310","DOI":"10.1007\/s10489-021-02438-8"},{"key":"4698_CR7","unstructured":"Jenatton R, Le\u00a0Roux N, Bordes A, Obozinski G (2012) A latent factor model for highly multi-relational data. In: Advances in Neural Information Processing Systems 25 (NIPS 2012), p 3176\u20133184"},{"key":"4698_CR8","unstructured":"Yang B, Yih SW\u00a0t, He X, Gao J, Deng L (2015) Embedding entities and relations for learning and inference in knowledge bases. In: Proceedings of the International Conference on Learning Representations (ICLR)"},{"key":"4698_CR9","unstructured":"Trouillon T, Welbl J, Riedel S, Gaussier \u00c9, Bouchard G (2016) Complex embeddings for simple link prediction. In: International Conference on Machine Learning, p 2071\u20132080"},{"key":"4698_CR10","unstructured":"Bordes A, Usunier N, Garcia-Duran A, Weston J, Yakhnenko O (2013) Translating embeddings for modeling multi-relational data. Advances in neural information processing systems 26"},{"issue":"5","key":"4698_CR11","doi-asserted-by":"publisher","first-page":"941","DOI":"10.1109\/TKDE.2019.2893920","volume":"32","author":"T Ebisu","year":"2019","unstructured":"Ebisu T, Ichise R (2019) Generalized translation-based embedding of knowledge graph. IEEE Transactions on Knowledge and Data Engineering 32(5):941\u2013951","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"4698_CR12","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":"4698_CR13","doi-asserted-by":"crossref","unstructured":"Lin Y, Liu Z, Sun M, Liu Y, Zhu X (2015) Learning entity and relation embeddings for knowledge graph completion. In: Twenty-ninth AAAI Conference on Artificial Intelligence","DOI":"10.1609\/aaai.v29i1.9491"},{"key":"4698_CR14","doi-asserted-by":"crossref","unstructured":"Yu M, Zhang Q, Yu J, Zhao M, Li X, Jin D, Yang M, Yu R (2022) Knowledge graph completion using topological correlation and multi-perspective independence. Knowledge-Based Systems, 110031","DOI":"10.1016\/j.knosys.2022.110031"},{"key":"4698_CR15","doi-asserted-by":"crossref","unstructured":"Perozzi B, Al-Rfou, R, Skiena S (2014) Deepwalk: Online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, p 701\u2013710","DOI":"10.1145\/2623330.2623732"},{"key":"4698_CR16","doi-asserted-by":"crossref","unstructured":"Grover A, Leskovec J (2016) node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International conference on knowledge discovery and data mining, p 855\u2013864","DOI":"10.1145\/2939672.2939754"},{"key":"4698_CR17","doi-asserted-by":"crossref","unstructured":"Tang J, Qu M, Wang M, Zhang M, Yan J, Mei Q (2015) Line: Large-scale information network embedding. In: Proceedings of the 24th International Conference on World Wide Web, p 1067\u20131077","DOI":"10.1145\/2736277.2741093"},{"issue":"2","key":"4698_CR18","doi-asserted-by":"publisher","first-page":"694","DOI":"10.1109\/TNNLS.2020.3028572","volume":"33","author":"G Guo","year":"2022","unstructured":"Guo G, Zhou H, Chen B, Liu Z, Xu X, Chen X, Dong Z, He X (2022) Ipgan: Generating informative item pairs by adversarial sampling. IEEE transactions on neural networks and learning systems 33(2):694\u2013706","journal-title":"IEEE transactions on neural networks and learning systems"},{"key":"4698_CR19","doi-asserted-by":"crossref","unstructured":"Chen J, Zhong M, Li J, Wang D, Qian T, Tu H (2021) Effective deep attributed network representation learning with topology adapted smoothing. IEEE Transactions on Cybernetics","DOI":"10.1109\/TCYB.2021.3064092"},{"key":"4698_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.108594","volume":"246","author":"H Chen","year":"2022","unstructured":"Chen H, Huang Z, Xu Y, Deng Z, Huang F, He P, Li Z (2022) Neighbor enhanced graph convolutional networks for node classification and recommendation. Knowledge-Based Systems 246:108594","journal-title":"Knowledge-Based Systems"},{"issue":"4","key":"4698_CR21","doi-asserted-by":"publisher","first-page":"233","DOI":"10.26599\/BDMA.2021.9020008","volume":"4","author":"J Zhang","year":"2021","unstructured":"Zhang J, Xu Q (2021) Attention-aware heterogeneous graph neural network. Big Data Mining and Analytics 4(4):233\u2013241","journal-title":"Big Data Mining and Analytics"},{"key":"4698_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.109631","volume":"256","author":"P Bielak","year":"2022","unstructured":"Bielak P, Kajdanowicz T, Chawla NV (2022) Graph barlow twins: A self-supervised representation learning framework for graphs. Knowledge-Based Systems 256:109631","journal-title":"Knowledge-Based Systems"},{"key":"4698_CR23","doi-asserted-by":"crossref","unstructured":"Ji G, Liu K, He S, Zhao J (2016) Knowledge graph completion with adaptive sparse transfer matrix. In: Thirtieth AAAI Conference on Artificial Intelligence","DOI":"10.1609\/aaai.v30i1.10089"},{"key":"4698_CR24","doi-asserted-by":"crossref","unstructured":"Xiao H, Huang M, Zhu X (2016) Transg: A generative model for knowledge graph embedding. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 2316\u20132325","DOI":"10.18653\/v1\/P16-1219"},{"key":"4698_CR25","doi-asserted-by":"crossref","unstructured":"Wang P, Li S, Pan R (2018) Incorporating gan for negative sampling in knowledge representation learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32","DOI":"10.1609\/aaai.v32i1.11536"},{"key":"4698_CR26","doi-asserted-by":"crossref","unstructured":"Cai L, Wang WY (2018) Kbgan: Adversarial learning for knowledge graph embeddings. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pp. 1470\u20131480","DOI":"10.18653\/v1\/N18-1133"},{"key":"4698_CR27","unstructured":"Gulrajani I, Ahmed F, Arjovsky M, Dumoulin V, Courville AC (2017) Improved training of wasserstein gans. Advances in neural information processing systems 30"},{"key":"4698_CR28","doi-asserted-by":"crossref","unstructured":"Qin S, Rao G, Bin C, Chang L, Gu T, Xuan W (2019) Knowledge graph embedding based on adaptive negative sampling. In: International Conference of Pioneering Computer Scientists, Engineers and Educators, pp 551\u2013563","DOI":"10.1007\/978-981-15-0118-0_42"},{"key":"4698_CR29","doi-asserted-by":"crossref","unstructured":"Zhang Y, Yao Q, Shao Y, Chen L (2019) Nscaching: Simple and efficient negative sampling for knowledge graph embedding. In: 2019 IEEE 35th International Conference on Data Engineering (ICDE), pp 614\u2013625","DOI":"10.1109\/ICDE.2019.00061"},{"issue":"2","key":"4698_CR30","doi-asserted-by":"publisher","first-page":"1188","DOI":"10.1007\/s10489-021-02287-5","volume":"52","author":"C Li","year":"2022","unstructured":"Li C, Chen H, Li T, Yang X (2022) A stable community detection approach for complex network based on density peak clustering and label propagation. Appl Intell 52(2):1188\u20131208","journal-title":"Appl Intell"},{"key":"4698_CR31","doi-asserted-by":"publisher","first-page":"675","DOI":"10.1016\/j.physa.2018.02.174","volume":"503","author":"J Ding","year":"2018","unstructured":"Ding J, He X, Yuan J, Chen Y, Jiang B (2018) Community detection by propagating the label of center. Physica A: Statistical Mechanics and its Applications 503:675\u2013686","journal-title":"Physica A: Statistical Mechanics and its Applications"},{"issue":"6","key":"4698_CR32","doi-asserted-by":"publisher","first-page":"21202","DOI":"10.1371\/journal.pone.0021202","volume":"6","author":"L L\u00fc","year":"2011","unstructured":"L\u00fc L, Zhang YC, Yeung CH, Zhou T (2011) Leaders in social networks, the delicious case. PloS one 6(6):21202","journal-title":"PloS one"},{"key":"4698_CR33","doi-asserted-by":"crossref","unstructured":"Church KW (2017) Word2vec. Natural Language Engineering 23(1):155\u2013162","DOI":"10.1017\/S1351324916000334"},{"key":"4698_CR34","doi-asserted-by":"crossref","unstructured":"Zhao F, Jin L, Yang LT, Jin H (2022) Relation and entropy weight-aware knowledge graph embedding for cloud manufacturing. IEEE Transactions on Industrial Informatics 18(12):9047\u20139056","DOI":"10.1109\/TII.2022.3178414"},{"issue":"4","key":"4698_CR35","doi-asserted-by":"publisher","first-page":"1118","DOI":"10.1073\/pnas.0706851105","volume":"105","author":"M Rosvall","year":"2008","unstructured":"Rosvall M, Bergstrom CT (2008) Maps of random walks on complex networks reveal community structure. Proceedings of the national academy of sciences 105(4):1118\u20131123","journal-title":"Proceedings of the national academy of sciences"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-04698-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-023-04698-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-04698-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,18]],"date-time":"2023-10-18T13:20:11Z","timestamp":1697635211000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-023-04698-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,24]]},"references-count":35,"journal-issue":{"issue":"19","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["4698"],"URL":"https:\/\/doi.org\/10.1007\/s10489-023-04698-y","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,24]]},"assertion":[{"value":"6 May 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 June 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Decleration"}},{"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":"Conflicts of interest"}}]}}