{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T21:58:32Z","timestamp":1782856712496,"version":"3.54.5"},"reference-count":33,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100003819","name":"Hubei Province Natural Science Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003819","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Knowledge-Based Systems"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.knosys.2026.116415","type":"journal-article","created":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T16:39:34Z","timestamp":1782491974000},"page":"116415","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["RealKGC: Relation-constrained large language models for inductive knowledge graph completion"],"prefix":"10.1016","volume":"349","author":[{"given":"Yan","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-3109-8800","authenticated-orcid":false,"given":"Jing","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenyi","family":"Xiong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-0382-6045","authenticated-orcid":false,"given":"Ziyue","family":"Qin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0443-0094","authenticated-orcid":false,"given":"Zhifei","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.knosys.2026.116415_b1","doi-asserted-by":"crossref","unstructured":"Xiao Huang, Jingyuan Zhang, Dingcheng Li, Ping Li, Knowledge Graph Embedding Based Question Answering, in: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, 2019, pp. 105\u2013113.","DOI":"10.1145\/3289600.3290956"},{"issue":"4","key":"10.1016\/j.knosys.2026.116415_b2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3447772","article-title":"Knowledge graphs","volume":"54","author":"Hogan","year":"2021","journal-title":"ACM Comput. Surv."},{"issue":"2","key":"10.1016\/j.knosys.2026.116415_b3","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1109\/TNNLS.2021.3070843","article-title":"A survey on knowledge graphs: Representation, acquisition, and applications","volume":"33","author":"Ji","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.knosys.2026.116415_b4","doi-asserted-by":"crossref","unstructured":"Jiajun Chen, Huarui He, Feng Wu, Jie Wang, Topology-Aware Correlations Between Relations for Inductive Link Prediction in Knowledge Graphs, in: Proceedings of the 35th AAAI Conference on Artificial Intelligence, 2021, pp. 6271\u20136278.","DOI":"10.1609\/aaai.v35i7.16779"},{"issue":"6","key":"10.1016\/j.knosys.2026.116415_b5","doi-asserted-by":"crossref","first-page":"707","DOI":"10.1007\/s00778-015-0394-1","article-title":"Fast rule mining in ontological knowledge bases with AMIE+","volume":"24","author":"Gal\u00e1rraga","year":"2015","journal-title":"VLDB J."},{"key":"10.1016\/j.knosys.2026.116415_b6","doi-asserted-by":"crossref","unstructured":"Christian Meilicke, Manuel Fink, Yanjie Wang, Daniel Ruffinelli, Rainer Gemulla, Heiner Stuckenschmidt, Fine-Grained Evaluation of Rule- and Embedding-Based Systems for Knowledge Graph Completion, in: Proceedings of the 17th International Semantic Web Conference, 2018, pp. 3\u201320.","DOI":"10.1007\/978-3-030-00671-6_1"},{"key":"10.1016\/j.knosys.2026.116415_b7","unstructured":"Ali Sadeghian, Mohammadreza Armandpour, Patrick Ding, Daisy Zhe Wang, DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphs, in: Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, Pages= 15321\u201315331, Year=2019."},{"key":"10.1016\/j.knosys.2026.116415_b8","unstructured":"Komal K. Teru, Etienne G. Denis, William L. Hamilton, Inductive Relation Prediction by Subgraph Reasoning, in: Proceedings of the Thirty-Seventh International Conference on Machine Learning, 2020, pp. 9448\u20139457."},{"key":"10.1016\/j.knosys.2026.116415_b9","unstructured":"Zhaocheng Zhu, Zuobai Zhang, Louis-Pascal A.C. Xhonneux, Jian Tang, Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction, in: Advances in Neural Information Processing Systems, 2021, pp. 29476\u201329490."},{"key":"10.1016\/j.knosys.2026.116415_b10","doi-asserted-by":"crossref","unstructured":"Yongqi Zhang, Quanming Yao, Knowledge Graph Reasoning with Relational Digraph, in: Proceedings of the ACM Web Conference, 2022, pp. 912\u2013924.","DOI":"10.1145\/3485447.3512008"},{"key":"10.1016\/j.knosys.2026.116415_b11","doi-asserted-by":"crossref","unstructured":"Liang Wang, Wei Zhao, Zhuoyu Wei, Jingming Liu, SimKGC: Simple Contrastive Knowledge Graph Completion with Pre-trained Language Models, in: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, 2022, pp. 4281\u20134294.","DOI":"10.18653\/v1\/2022.acl-long.295"},{"key":"10.1016\/j.knosys.2026.116415_b12","doi-asserted-by":"crossref","unstructured":"Jason Youn, Ilias Tagkopoulos, KGLM: Integrating Knowledge Graph Structure in Language Models for Link Prediction, in: Proceedings of the 12th Joint Conference on Lexical and Computational Semantics (*SEM 2023), 2023, pp. 217\u2013224.","DOI":"10.18653\/v1\/2023.starsem-1.20"},{"issue":"10","key":"10.1016\/j.knosys.2026.116415_b13","doi-asserted-by":"crossref","first-page":"6202","DOI":"10.1109\/TKDE.2025.3591056","article-title":"Thinking on context: Inductive relation prediction guided by the reasoning ability of large language models","volume":"37","author":"Chen","year":"2025","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.knosys.2026.116415_b14","doi-asserted-by":"crossref","unstructured":"Sijie Mai, Shuangjia Zheng, Yuedong Yang, Haifeng Hu, Communicative Message Passing for Inductive Relation Reasoning, in: Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021, pp. 4294\u20134302.","DOI":"10.1609\/aaai.v35i5.16554"},{"key":"10.1016\/j.knosys.2026.116415_b15","unstructured":"Liang Yao, Chengsheng Mao, Yuan Luo, KG-BERT: BERT for Knowledge Graph Completion, 2019, CoRR."},{"key":"10.1016\/j.knosys.2026.116415_b16","doi-asserted-by":"crossref","unstructured":"Apoorv Saxena, Adrian Kochsiek, Rainer Gemulla, Sequence-to-Sequence Knowledge Graph Completion and Question Answering, in: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, 2022, pp. 2814\u20132828.","DOI":"10.18653\/v1\/2022.acl-long.201"},{"key":"10.1016\/j.knosys.2026.116415_b17","doi-asserted-by":"crossref","unstructured":"Chen Chen, Yufei Wang, Aixin Sun, Bing Li, Kwok-Yan Lam, Dipping PLMs Sauce: Bridging Structure and Text for Effective Knowledge Graph Completion via Conditional Soft Prompting, in: Findings of the Association for Computational Linguistics, 2023, pp. 11489\u201311503.","DOI":"10.18653\/v1\/2023.findings-acl.729"},{"key":"10.1016\/j.knosys.2026.116415_b18","doi-asserted-by":"crossref","unstructured":"Yichi Zhang, Zhuo Chen, Lingbing Guo, Yajing Xu, Wen Zhang, Huajun Chen, Making Large Language Models Perform Better in Knowledge Graph Completion, in: Proceedings of the 32nd ACM International Conference on Multimedia, 2024, pp. 233\u2013242.","DOI":"10.1145\/3664647.3681327"},{"key":"10.1016\/j.knosys.2026.116415_b19","series-title":"Findings of the Association for Computational Linguistics: EMNLP 2023","first-page":"8667","article-title":"KICGPT: Large language model with knowledge in context for knowledge graph completion","author":"Wei","year":"2023"},{"key":"10.1016\/j.knosys.2026.116415_b20","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2024.102868","article-title":"GS-KGC: a generative subgraph-based framework for knowledge graph completion with large language models","volume":"117","author":"Yang","year":"2025","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.knosys.2026.116415_b21","doi-asserted-by":"crossref","unstructured":"Yongkang Xiao, Sinian Zhang, Yi Dai, Huixue Zhou, Jue Hou, Jie Ding, Rui Zhang, DrKGC: Dynamic Subgraph Retrieval-Augmented LLMs for Knowledge Graph Completion across General and Biomedical Domains, in: Findings of the Association for Computational Linguistics: EMNLP 2025, Suzhou, China, November 4-9, 2025, 2025, pp. 16432\u201316445.","DOI":"10.18653\/v1\/2025.findings-emnlp.892"},{"key":"10.1016\/j.knosys.2026.116415_b22","doi-asserted-by":"crossref","unstructured":"Muzhi Li, Cehao Yang, Chengjin Xu, Zixing Song, Xuhui Jiang, Jian Guo, Ho fung Leung, Irwin King, Context-aware Inductive Knowledge Graph Completion with Latent Type Constraints and Subgraph Reasoning, in: Proceedings of the 39th AAAI Conference on Artificial Intelligence, 2025, pp. 12102\u201312111.","DOI":"10.1609\/aaai.v39i11.33318"},{"key":"10.1016\/j.knosys.2026.116415_b23","doi-asserted-by":"crossref","unstructured":"Jianlyu Chen, Shitao Xiao, Peitian Zhang, Kun Luo, Defu Lian, Zheng Liu, M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation, in: Findings of the Association for Computational Linguistics, 2024, pp. 2318\u20132335.","DOI":"10.18653\/v1\/2024.findings-acl.137"},{"key":"10.1016\/j.knosys.2026.116415_b24","series-title":"Qwen2 technical report","author":"Yang","year":"2024"},{"key":"10.1016\/j.knosys.2026.116415_b25","doi-asserted-by":"crossref","unstructured":"Yongqi Zhang, Zhanke Zhou, Quanming Yao, Xiaowen Chu, Bo Han, AdaProp: Learning Adaptive Propagation for Graph Neural Network based Knowledge Graph Reasoning, in: Proceedings of the Twenty-Ninth ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023, pp. 3446\u20133457.","DOI":"10.1145\/3580305.3599404"},{"key":"10.1016\/j.knosys.2026.116415_b26","doi-asserted-by":"crossref","unstructured":"Hanwen Zha, Zhiyu Chen, Xifeng Yan, Inductive Relation Prediction by BERT, in: Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022, pp. 5923\u20135931.","DOI":"10.1609\/aaai.v36i5.20537"},{"key":"10.1016\/j.knosys.2026.116415_b27","doi-asserted-by":"crossref","unstructured":"Zile Qiao, Wei Ye, Dingyao Yu, Tong Mo, Weiping Li, Shikun Zhang, Improving Knowledge Graph Completion with Generative Hard Negative Mining, in: Findings of the Association for Computational Linguistics, 2023, pp. 5866\u20135878.","DOI":"10.18653\/v1\/2023.findings-acl.362"},{"key":"10.1016\/j.knosys.2026.116415_b28","doi-asserted-by":"crossref","unstructured":"Zhixiang Su, Di Wang, Chunyan Miao, Lizhen Cui, Multi-Aspect Explainable Inductive Relation Prediction by Sentence Transformer, in: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023, pp. 6533\u20136540.","DOI":"10.1609\/aaai.v37i5.25803"},{"key":"10.1016\/j.knosys.2026.116415_b29","doi-asserted-by":"crossref","unstructured":"Zhixiang Su, Di Wang, Chunyan Miao, Lizhen Cui, Anchoring Path for Inductive Relation Prediction in Knowledge Graphs, in: Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024, pp. 9011\u20139018.","DOI":"10.1609\/aaai.v38i8.28750"},{"key":"10.1016\/j.knosys.2026.116415_b30","doi-asserted-by":"crossref","unstructured":"Dawei Li, Zhen Tan, Tianlong Chen, Huan Liu, Contextualization Distillation from Large Language Model for Knowledge Graph Completion, in: Findings of the Association for Computational Linguistics, 2024, pp. 458\u2013477.","DOI":"10.18653\/v1\/2024.findings-eacl.32"},{"key":"10.1016\/j.knosys.2026.116415_b31","doi-asserted-by":"crossref","unstructured":"Liang Yao, Jiazhen Peng, Chengsheng Mao, Yuan Luo, Exploring Large Language Models for Knowledge Graph Completion, in: Proceedings of the 2025 IEEE International Conference on Acoustics, Speech and Signal Processing, 2025, pp. 1\u20135.","DOI":"10.1109\/ICASSP49660.2025.10889242"},{"key":"10.1016\/j.knosys.2026.116415_b32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TKDE.2026.3684254","article-title":"KICGPTv2: Large language model with knowledge in context for knowledge graph completion","author":"Wei","year":"2026","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.knosys.2026.116415_b33","doi-asserted-by":"crossref","first-page":"1027","DOI":"10.1162\/tacl_a_00686","article-title":"Multi-level shared knowledge guided learning for knowledge graph completion","volume":"12","author":"Shan","year":"2024","journal-title":"Trans. Assoc. Comput. Linguist."}],"container-title":["Knowledge-Based Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S095070512601141X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S095070512601141X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T20:50:52Z","timestamp":1782852652000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S095070512601141X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":33,"alternative-id":["S095070512601141X"],"URL":"https:\/\/doi.org\/10.1016\/j.knosys.2026.116415","relation":{},"ISSN":["0950-7051"],"issn-type":[{"value":"0950-7051","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"RealKGC: Relation-constrained large language models for inductive knowledge graph completion","name":"articletitle","label":"Article Title"},{"value":"Knowledge-Based Systems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.knosys.2026.116415","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"116415"}}