{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T10:46:29Z","timestamp":1776768389363,"version":"3.51.2"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2023,5,19]],"date-time":"2023-05-19T00:00:00Z","timestamp":1684454400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,5,19]],"date-time":"2023-05-19T00:00:00Z","timestamp":1684454400000},"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":["62072087"],"award-info":[{"award-number":["62072087"]}],"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":["62002054"],"award-info":[{"award-number":["62002054"]}],"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":["61932004"],"award-info":[{"award-number":["61932004"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["World Wide Web"],"published-print":{"date-parts":[[2023,9]]},"DOI":"10.1007\/s11280-023-01167-x","type":"journal-article","created":{"date-parts":[[2023,5,19]],"date-time":"2023-05-19T10:02:18Z","timestamp":1684490538000},"page":"2887-2907","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Correlation embedding learning with dynamic semantic enhanced sampling for knowledge graph completion"],"prefix":"10.1007","volume":"26","author":[{"given":"Haojie","family":"Nie","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangguo","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Bi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuliang","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"George Y.","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,5,19]]},"reference":[{"issue":"3","key":"1167_CR1","doi-asserted-by":"publisher","first-page":"1103","DOI":"10.1007\/s11280-022-01022-5","volume":"25","author":"J Wang","year":"2022","unstructured":"Wang, J., Shi, Y., Li, D., Zhang, K., Chen, Z., Li, H.: Mcha a multistage clustering-based hierarchical attention model for knowledge graph-aware recommendation. World Wide Web 25(3), 1103\u20131127 (2022)","journal-title":"World Wide Web"},{"issue":"5","key":"1167_CR2","doi-asserted-by":"publisher","first-page":"1769","DOI":"10.1007\/s11280-021-00912-4","volume":"24","author":"Y Huang","year":"2021","unstructured":"Huang, Y., Zhao, F., Gui, X., Gui, H.: Path-enhanced explainable recommendation with knowledge graphs. World Wide Web. 24(5), 1769\u20131789 (2021)","journal-title":"World Wide Web."},{"issue":"5","key":"1167_CR3","doi-asserted-by":"publisher","first-page":"1837","DOI":"10.1007\/s11280-021-00911-5","volume":"24","author":"J Liao","year":"2021","unstructured":"Liao, J., Zhao, X., Tang, J., Zeng, W., Tan, Z.: To hop or not, that is the question Towards effective multi-hop reasoning over knowledge graphs. World Wide Web. 24(5), 1837\u20131856 (2021)","journal-title":"World Wide Web."},{"issue":"2","key":"1167_CR4","doi-asserted-by":"publisher","first-page":"1005","DOI":"10.1007\/s11280-021-00965-5","volume":"25","author":"Q Mehmood","year":"2022","unstructured":"Mehmood, Q., Saleem, M., Jha, A., d\u2019Aquin, M.: Efficient distributed path computation on RDF knowledge graphs using partial evaluation. World Wide Web. 25(2), 1005\u20131036 (2022)","journal-title":"World Wide Web."},{"key":"1167_CR5","doi-asserted-by":"crossref","unstructured":"Cai, B., Xiang, Y., Gao, L., Zhang, H., Li, Y., Li, J.: Temporal knowledge graph completion: A survey. CoRR arXiv:2201.08236 (2022)","DOI":"10.24963\/ijcai.2023\/734"},{"key":"1167_CR6","doi-asserted-by":"publisher","first-page":"212","DOI":"10.1016\/j.neucom.2021.03.138","volume":"472","author":"G Xue","year":"2022","unstructured":"Xue, G., Zhong, M., Li, J., Chen, J., Zhai, C., Kong, R.: Dynamic network embedding survey. Neurocomputing. 472, 212\u2013223 (2022)","journal-title":"Neurocomputing."},{"key":"1167_CR7","doi-asserted-by":"crossref","unstructured":"Bollacker, K., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: Freebase a collaboratively created graph database for structuring human knowledge. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, pp. 1247\u20131250 (2008)","DOI":"10.1145\/1376616.1376746"},{"key":"1167_CR8","doi-asserted-by":"crossref","unstructured":"Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.: Dbpedia A nucleus for a web of open data. In: The Semantic Web, pp. 722\u2013735 (2007)","DOI":"10.1007\/978-3-540-76298-0_52"},{"key":"1167_CR9","doi-asserted-by":"crossref","unstructured":"Suchanek, F.M., Kasneci, G., Weikum, G.: Yago a core of semantic knowledge. In: Proceedings of the 16th International Conference on World Wide Web, pp. 697\u2013706 (2007)","DOI":"10.1145\/1242572.1242667"},{"issue":"1","key":"1167_CR10","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1109\/JPROC.2015.2483592","volume":"104","author":"M Nickel","year":"2016","unstructured":"Nickel, M., Murphy, K., Tresp, V., Gabrilovich, E.: A review of relational machine learning for knowledge graphs. Proc. IEEE 104(1), 11\u201333 (2016)","journal-title":"Proc. IEEE"},{"key":"1167_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.119122","volume":"214","author":"T Le","year":"2023","unstructured":"Le, T., Le, N., Le, B.: Knowledge graph embedding by relational rotation and complex convolution for link prediction. Expert Syst. Appl. 214, 119122 (2023)","journal-title":"Expert Syst. Appl."},{"issue":"3","key":"1167_CR12","first-page":"2486","volume":"35","author":"S Liang","year":"2023","unstructured":"Liang, S., Shao, J., Zhang, D., Zhang, J., Cui, B.: DRGI deep relational graph infomax for knowledge graph completion. IEEE Trans. Knowl. Data Eng. 35(3), 2486\u20132499 (2023)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"1167_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.109597","volume":"255","author":"T Shen","year":"2022","unstructured":"Shen, T., Zhang, F., Cheng, J.: A comprehensive overview of knowledge graph completion. Knowl. Based Syst. 255, 109597 (2022)","journal-title":"Knowl. Based Syst."},{"key":"1167_CR14","unstructured":"Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. Advances in neural information processing systems. 26 (2013)"},{"key":"1167_CR15","doi-asserted-by":"crossref","unstructured":"Stoica, G., Stretcu, O., Platanios, E.A., Mitchell, T., P\u00f3czos, B.: Contextual parameter generation for knowledge graph link prediction. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 3000\u20133008 (2020)","DOI":"10.1609\/aaai.v34i03.5693"},{"key":"1167_CR16","doi-asserted-by":"crossref","unstructured":"Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: Twenty-ninth AAAI Conference on Artificial Intelligence (2015)","DOI":"10.1609\/aaai.v29i1.9491"},{"key":"1167_CR17","doi-asserted-by":"crossref","unstructured":"Ji, G., He, S., Xu, L., Liu, K., Zhao, J.: 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 vol.1, pp. 687\u2013696 (2015)","DOI":"10.3115\/v1\/P15-1067"},{"key":"1167_CR18","doi-asserted-by":"crossref","unstructured":"Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.28 (2014)","DOI":"10.1609\/aaai.v28i1.8870"},{"key":"1167_CR19","doi-asserted-by":"crossref","unstructured":"Xiao, C., Li, B., Zhu, J.-Y., He, W., Liu, M., Song, D.: Generating adversarial examples with adversarial networks. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence, pp. 3905\u20133911 (2018)","DOI":"10.24963\/ijcai.2018\/543"},{"key":"1167_CR20","doi-asserted-by":"crossref","unstructured":"Wang, P., Li, S., Pan, R.: Incorporating gan for negative sampling in knowledge representation learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)","DOI":"10.1609\/aaai.v32i1.11536"},{"key":"1167_CR21","doi-asserted-by":"crossref","unstructured":"Cai, L., Wang, W.Y.: 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, Vol.1, pp. 1470\u20131480 (2018)","DOI":"10.18653\/v1\/N18-1133"},{"key":"1167_CR22","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Yao, Q., Shao, Y., Chen, L.: Nscaching simple and efficient negative sampling for knowledge graph embedding. In: 2019 IEEE 35th International Conference on Data Engineering (ICDE), pp. 614\u2013625 (2019)","DOI":"10.1109\/ICDE.2019.00061"},{"key":"1167_CR23","unstructured":"Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.C.: Improved training of wasserstein gans. Advances in neural information processing systems 30 (2017)"},{"key":"1167_CR24","doi-asserted-by":"crossref","unstructured":"Yang, S., Tian, J., Zhang, H., Yan, J., He, H., Jin, Y.: Transms Knowledge graph embedding for complex relations by multidirectional semantics. In: IJCAI, pp. 1935\u20131942 (2019)","DOI":"10.24963\/ijcai.2019\/268"},{"key":"1167_CR25","unstructured":"Cui, Z., Liu, S., Pan, L., He, Q.: Translating embedding with local connection for knowledge graph completion. In: Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems, pp. 1825\u20131827 (2020)"},{"key":"1167_CR26","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Cai, J., Zhang, Y., Wang, J.: Learning hierarchy-aware knowledge graph embeddings for link prediction. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 3065\u20133072 (2020)","DOI":"10.1609\/aaai.v34i03.5701"},{"key":"1167_CR27","unstructured":"Yang, B., Yih, S.W.-t., He, X., Gao, J., Deng, L.: Embedding entities and relations for learning and inference in knowledge bases. In: Proceedings of the International Conference on Learning Representations (ICLR) 2015 (2015)"},{"key":"1167_CR28","unstructured":"Trouillon, T., Welbl, J., Riedel, S., Gaussier, \u00c9., Bouchard, G.: Complex embeddings for simple link prediction. In: International Conference on Machine Learning, pp. 2071\u20132080 (2016)"},{"key":"1167_CR29","doi-asserted-by":"crossref","unstructured":"Nickel, M., Rosasco, L., Poggio, T.: Holographic embeddings of knowledge graphs. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 30 (2016)","DOI":"10.1609\/aaai.v30i1.10314"},{"key":"1167_CR30","doi-asserted-by":"crossref","unstructured":"Perozzi, B., Al-Rfou, R., Skiena, S.: Deepwalk Online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701\u2013710 (2014)","DOI":"10.1145\/2623330.2623732"},{"key":"1167_CR31","doi-asserted-by":"crossref","unstructured":"Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: Line Large-scale information network embedding. In: Proceedings of the 24th International Conference on World Wide Web, pp. 1067\u20131077 (2015)","DOI":"10.1145\/2736277.2741093"},{"key":"1167_CR32","doi-asserted-by":"crossref","unstructured":"Wang, D., Cui, P., Zhu, W.: Structural deep network embedding. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1225\u20131234 (2016)","DOI":"10.1145\/2939672.2939753"},{"key":"1167_CR33","doi-asserted-by":"crossref","unstructured":"Dettmers, T., Minervini, P., Stenetorp, P., Riedel, S.: Convolutional 2d knowledge graph embeddings. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)","DOI":"10.1609\/aaai.v32i1.11573"},{"key":"1167_CR34","doi-asserted-by":"crossref","unstructured":"Nguyen, T.D., Nguyen, D.Q., Phung, D., et al.: A novel embedding model for knowledge base completion based on convolutional neural network. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Vol. 2, pp. 327\u2013333 (2018)","DOI":"10.18653\/v1\/N18-2053"},{"key":"1167_CR35","doi-asserted-by":"crossref","unstructured":"Shang, C., Tang, Y., Huang, J., Bi, J., He, X., Zhou, B.: End-to-end structure-aware convolutional networks for knowledge base completion. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.33, pp. 3060\u20133067 (2019)","DOI":"10.1609\/aaai.v33i01.33013060"},{"key":"1167_CR36","doi-asserted-by":"crossref","unstructured":"Vu, T., Nguyen, T.D., Nguyen, D.Q., Phung, D., et al.: A capsule network-based embedding model for knowledge graph completion and search personalization. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Vol. 1, pp. 2180\u20132189 (2019)","DOI":"10.18653\/v1\/N19-1226"},{"key":"1167_CR37","doi-asserted-by":"crossref","unstructured":"Nathani, D., Chauhan, J., Sharma, C., Kaul, M.: Learning attention-based embeddings for relation prediction in knowledge graphs. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 4710\u20134723 (2019)","DOI":"10.18653\/v1\/P19-1466"},{"key":"1167_CR38","doi-asserted-by":"crossref","unstructured":"Guo, L., Zhang, Q., Ge, W., Hu, W., Qu, Y.: Dskg A deep sequential model for knowledge graph completion. In: China Conference on Knowledge Graph and Semantic Computing, pp. 65\u201377 (2018)","DOI":"10.1007\/978-981-13-3146-6_6"},{"key":"1167_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.108515","volume":"243","author":"X Bi","year":"2022","unstructured":"Bi, X., Nie, H., Zhang, X., Zhao, X., Yuan, Y., Wang, G.: Unrestricted multi-hop reasoning network for interpretable question answering over knowledge graph. Knowl. Based Syst. 243, 108515 (2022)","journal-title":"Knowl. Based Syst."}],"container-title":["World Wide Web"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11280-023-01167-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11280-023-01167-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11280-023-01167-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,11]],"date-time":"2023-10-11T04:21:16Z","timestamp":1696998076000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11280-023-01167-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,19]]},"references-count":39,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2023,9]]}},"alternative-id":["1167"],"URL":"https:\/\/doi.org\/10.1007\/s11280-023-01167-x","relation":{},"ISSN":["1386-145X","1573-1413"],"issn-type":[{"value":"1386-145X","type":"print"},{"value":"1573-1413","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,19]]},"assertion":[{"value":"19 October 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 February 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 March 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 May 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":"not applicable","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}},{"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":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}