{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T18:38:08Z","timestamp":1769020688442,"version":"3.49.0"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"5-6","license":[{"start":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T00:00:00Z","timestamp":1730246400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T00:00:00Z","timestamp":1730246400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Yunnan provincial major science and technology special plan projects","award":["Grant NO.202202AD080004"],"award-info":[{"award-number":["Grant NO.202202AD080004"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1007\/s13042-024-02434-7","type":"journal-article","created":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T15:17:28Z","timestamp":1730301448000},"page":"3035-3048","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Relational semantic-enhanced logic rule learning for knowledge graph completion"],"prefix":"10.1007","volume":"16","author":[{"given":"Yuxin","family":"Huang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiyong","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Xiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,10,30]]},"reference":[{"issue":"1","key":"2434_CR1","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1109\/JPROC.2015.2483592","volume":"104","author":"M Nickel","year":"2015","unstructured":"Nickel M, Murphy K, Tresp V et al (2015) A review of relational machine learning for knowledge graphs. Proc IEEE 104(1):11\u201333","journal-title":"Proc IEEE"},{"issue":"12","key":"2434_CR2","doi-asserted-by":"publisher","first-page":"2724","DOI":"10.1109\/TKDE.2017.2754499","volume":"29","author":"Q Wang","year":"2017","unstructured":"Wang Q, Mao Z, Wang B et al (2017) Knowledge graph embedding: a survey of approaches and applications. IEEE Trans Knowl Data Eng 29(12):2724\u20132743","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2434_CR3","unstructured":"Lin Y, Han X, Xie R et al (2018) Knowledge representation learning: a quantitative review. CoRR arXiv:1812.10901"},{"key":"2434_CR4","doi-asserted-by":"crossref","unstructured":"Ru D, Sun C, Feng J et al (2021) Learning logic rules for document-level relation extraction. CoRR arXiv:2111.05407","DOI":"10.18653\/v1\/2021.emnlp-main.95"},{"key":"2434_CR5","doi-asserted-by":"crossref","unstructured":"Chen Y, Goldberg S, Wang DZ et al (2016) Ontological pathfinding. In: Proceedings of the 2016 international conference on management of data, pp 835\u2013846","DOI":"10.1145\/2882903.2882954"},{"key":"2434_CR6","doi-asserted-by":"crossref","unstructured":"Cheng K, Liu J, Wang W et al (2022) RLogic: recursive logical rule learning from knowledge graphs. In: Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining, pp 179\u2013189","DOI":"10.1145\/3534678.3539421"},{"key":"2434_CR7","unstructured":"Qu M, Chen J, Xhonneux LP et\u00a0al (2021) RNNLogic: learning logic rules for reasoning on knowledge graphs. In: International conference on learning representations"},{"key":"2434_CR8","doi-asserted-by":"crossref","unstructured":"Cheng K, Ahmed N, Sun Y (2023) Neural compositional rule learning for knowledge graph reasoning. In: The eleventh international conference on learning representations","DOI":"10.1007\/978-3-031-72008-6_5"},{"issue":"7","key":"2434_CR9","doi-asserted-by":"publisher","first-page":"1909","DOI":"10.1007\/s10115-022-01691-8","volume":"64","author":"L Zhang","year":"2022","unstructured":"Zhang L, Wang F, Jiang H et al (2022) Random walk on node cliques for high-quality samples to estimate large graphs with high accuracies and low costs. Knowl Inf Syst 64(7):1909\u20131935","journal-title":"Knowl Inf Syst"},{"issue":"7","key":"2434_CR10","doi-asserted-by":"publisher","first-page":"3580","DOI":"10.1109\/TKDE.2024.3352100","volume":"36","author":"S Pan","year":"2024","unstructured":"Pan S, Luo L, Wang Y et al (2024) Unifying large language models and knowledge graphs: a roadmap. IEEE Trans Knowl Data Eng 36(7):3580\u20133599","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2434_CR11","unstructured":"Bordes A, Usunier N, Garcia-Duran A et\u00a0al (2013) Translating embeddings for modeling multi-relational data. In: Advances in neural information processing systems, vol 26"},{"key":"2434_CR12","doi-asserted-by":"crossref","unstructured":"Wang Z, Zhang J, Feng J et\u00a0al (2014) Knowledge graph embedding by translating on hyperplanes. In: Proceedings of the AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v28i1.8870"},{"key":"2434_CR13","doi-asserted-by":"crossref","unstructured":"Lin Y, Liu Z, Sun M et\u00a0al (2015) Learning entity and relation embeddings for knowledge graph completion. In: Proceedings of the AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v29i1.9491"},{"key":"2434_CR14","unstructured":"Sun Z, Deng Z, Nie J et\u00a0al (2019) RotatE: knowledge graph embedding by relational rotation in complex space. CoRR arXiv:1902.10197"},{"key":"2434_CR15","unstructured":"Nickel M, Tresp V, Kriegel HP et al (2011) A three-way model for collective learning on multi-relational data. In: International conference on machine learning, pp 3104482\u20133104584"},{"key":"2434_CR16","unstructured":"Yang B, Yih W, He X et\u00a0al (2015) Embedding entities and relations for learning and inference in knowledge bases. In: 3rd International conference on learning representations, international conference on learning representations 2015, San Diego, CA, USA, May 7\u20139, 2015, conference track proceedings"},{"key":"2434_CR17","doi-asserted-by":"crossref","unstructured":"Montella S, Rojas-Barahona LM, Heinecke J (2021) Hyperbolic temporal knowledge graph embeddings with relational and time curvatures. CoRR arXiv:2106.04311","DOI":"10.18653\/v1\/2021.findings-acl.292"},{"key":"2434_CR18","doi-asserted-by":"crossref","unstructured":"Khot T, Natarajan S, Kersting K et\u00a0al (2011) Learning Markov logic networks via functional gradient boosting. In: 2011 IEEE 11th international conference on data mining. IEEE, pp 320\u2013329","DOI":"10.1109\/ICDM.2011.87"},{"key":"2434_CR19","unstructured":"Natarajan S, Khot T, Kersting K et\u00a0al (2010) Boosting relational dependency networks. In: Proceedings of the international conference on inductive logic programming (ILP)"},{"key":"2434_CR20","unstructured":"Yang F, Yang Z, Cohen WW (2017) Differentiable learning of logical rules for knowledge base reasoning. In: Advances in neural information processing systems, vol 30"},{"key":"2434_CR21","unstructured":"Sadeghian A, Armandpour M, Ding P et\u00a0al (2019) DRUM: end-to-end differentiable rule mining on knowledge graphs. In: Advances in neural information processing systems, vol 32"},{"key":"2434_CR22","unstructured":"Wang PW, Stepanova D, Domokos C et\u00a0al (2020) Differentiable learning of numerical rules in knowledge graphs. In: International conference on learning representations"},{"key":"2434_CR23","doi-asserted-by":"crossref","unstructured":"Gal\u00e1rraga LA, Teflioudi C, Hose K et\u00a0al (2013) Amie: association rule mining under incomplete evidence in ontological knowledge bases. In: Proceedings of the 22nd international conference on World Wide Web, pp 413\u2013422","DOI":"10.1145\/2488388.2488425"},{"issue":"6","key":"2434_CR24","doi-asserted-by":"publisher","first-page":"707","DOI":"10.1007\/s00778-015-0394-1","volume":"24","author":"L Gal\u00e1rraga","year":"2015","unstructured":"Gal\u00e1rraga L, Teflioudi C, Hose K et al (2015) Fast rule mining in ontological knowledge bases with AMIE+. VLDB J 24(6):707\u2013730","journal-title":"VLDB J"},{"key":"2434_CR25","doi-asserted-by":"crossref","unstructured":"Meilicke C, Chekol MW, Fink M et\u00a0al (2020) Reinforced anytime bottom up rule learning for knowledge graph completion. CoRR arXiv:2004.04412","DOI":"10.24963\/ijcai.2019\/435"},{"key":"2434_CR26","doi-asserted-by":"crossref","unstructured":"Ortona S, Meduri VV, Papotti P (2018) Robust discovery of positive and negative rules in knowledge bases. In: 2018 IEEE 34th international conference on data engineering (ICDE). IEEE, pp 1168\u20131179","DOI":"10.1109\/ICDE.2018.00108"},{"key":"2434_CR27","doi-asserted-by":"crossref","unstructured":"Chen S, Liu X, Gao J et\u00a0al (2021) HittER: hierarchical transformers for knowledge graph embeddings. In: Proceedings of the 2021 conference on empirical methods in natural language processing, Online and Punta Cana, Dominican Republic","DOI":"10.18653\/v1\/2021.emnlp-main.812"},{"key":"2434_CR28","doi-asserted-by":"crossref","unstructured":"Lin Q, Liu J, Xu F et\u00a0al (2022) Incorporating context graph with logical reasoning for inductive relation prediction. In: Proceedings of the 45th international ACM SIGIR conference on research and development in information retrieval. pp 893\u2013903","DOI":"10.1145\/3477495.3531996"},{"key":"2434_CR29","doi-asserted-by":"crossref","unstructured":"Omran PG, Wang K, Wang Z (2018) Scalable rule learning via learning representation. In: International joint conference on artificial intelligence. pp 2149\u20132155","DOI":"10.24963\/ijcai.2018\/297"},{"key":"2434_CR30","unstructured":"Lu S, Liu B, Mills KG et\u00a0al (2022) R5: rule discovery with reinforced and recurrent relational reasoning. In: International conference on learning representations"},{"issue":"12","key":"2434_CR31","doi-asserted-by":"publisher","first-page":"145","DOI":"10.23919\/JCC.2019.12.011","volume":"16","author":"A Razzaq","year":"2019","unstructured":"Razzaq A, Asim M, Ali Z et al (2019) Text sentiment analysis using frequency-based vigorous features. China Commun 16(12):145\u2013153","journal-title":"China Commun"},{"key":"2434_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2020.124289","volume":"553","author":"A Kumar","year":"2020","unstructured":"Kumar A, Singh SS, Singh K et al (2020) Link prediction techniques, applications, and performance: A survey. Physica A Stat Mech Appl 553:124289","journal-title":"Physica A Stat Mech Appl"},{"key":"2434_CR33","doi-asserted-by":"crossref","unstructured":"Kok S, Domingos P (2007) Statistical predicate invention. In: Proceedings of the 24th international conference on machine learning, pp 433\u2013440","DOI":"10.1145\/1273496.1273551"},{"key":"2434_CR34","unstructured":"Hinton GE et\u00a0al (1986) Learning distributed representations of concepts. In: Proceedings of the eighth annual conference of the cognitive science society, Amherst, MA, p\u00a012"},{"key":"2434_CR35","doi-asserted-by":"crossref","unstructured":"Burges C, Ragno R, Le Q (2006) Learning to rank with nonsmooth cost functions. In: Advances in neural information processing systems, vol 19","DOI":"10.7551\/mitpress\/7503.003.0029"},{"key":"2434_CR36","unstructured":"Trouillon T, Welbl J, Riedel S et\u00a0al (2016) Complex embeddings for simple link prediction. In: International conference on machine learning. PMLR, pp 2071\u20132080"},{"key":"2434_CR37","doi-asserted-by":"crossref","unstructured":"Zhang Z, Cai J, Zhang Y et\u00a0al (2020) Learning hierarchy-aware knowledge graph embeddings for link prediction. In: Proceedings of the AAAI conference on artificial intelligence, pp 3065\u20133072","DOI":"10.1609\/aaai.v34i03.5701"},{"key":"2434_CR38","unstructured":"Han C, He Q, Yu C et\u00a0al (2023) Logical entity representation in knowledge-graphs for differentiable rule learning. In: The Eleventh international conference on learning representations"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02434-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-024-02434-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02434-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,7]],"date-time":"2025-06-07T04:29:07Z","timestamp":1749270547000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-024-02434-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,30]]},"references-count":38,"journal-issue":{"issue":"5-6","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["2434"],"URL":"https:\/\/doi.org\/10.1007\/s13042-024-02434-7","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,30]]},"assertion":[{"value":"14 June 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 October 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 October 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}