{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T02:02:34Z","timestamp":1780020154120,"version":"3.53.1"},"reference-count":62,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Knowledge-Based Systems"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.knosys.2026.116161","type":"journal-article","created":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T15:30:17Z","timestamp":1778945417000},"page":"116161","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Multi-Condition Latent Diffusion Network for Semantic-aware Knowledge Graph Completion"],"prefix":"10.1016","volume":"346","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-0035-5295","authenticated-orcid":false,"given":"Siyuan","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5055-1527","authenticated-orcid":false,"given":"Liang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Jin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaopeng","family":"Wei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.knosys.2026.116161_b1","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2022.109597","article-title":"A comprehensive overview of knowledge graph completion","volume":"255","author":"Shen","year":"2022","journal-title":"Knowl.-Based Syst.","ISSN":"https:\/\/id.crossref.org\/issn\/0950-7051","issn-type":"print"},{"key":"10.1016\/j.knosys.2026.116161_b2","series-title":"International Conference on Blended Learning","first-page":"358","article-title":"Kcube: A knowledge graph university curriculum framework for student advising and career planning","author":"Li","year":"2022"},{"key":"10.1016\/j.knosys.2026.116161_b3","series-title":"International Conference on Web-Based Learning","first-page":"148","article-title":"Constructing low-redundant and high-accuracy knowledge graphs for education","author":"Li","year":"2022"},{"key":"10.1016\/j.knosys.2026.116161_b4","doi-asserted-by":"crossref","unstructured":"Junnan Dong, Qinggang Zhang, Xiao Huang, Keyu Duan, Qiaoyu Tan, Zhimeng Jiang, Hierarchy-aware multi-hop question answering over knowledge graphs, in: Proceedings of the ACM Web Conference 2023, 2023, pp. 2519\u20132527.","DOI":"10.1145\/3543507.3583376"},{"issue":"12","key":"10.1016\/j.knosys.2026.116161_b5","doi-asserted-by":"crossref","first-page":"2724","DOI":"10.1109\/TKDE.2017.2754499","article-title":"Knowledge graph embedding: A survey of approaches and applications","volume":"29","author":"Wang","year":"2017","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"2","key":"10.1016\/j.knosys.2026.116161_b6","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":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.knosys.2026.116161_b7","article-title":"Integrating entity attributes for error-aware knowledge graph embedding","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.knosys.2026.116161_b8","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2019.112948","article-title":"A review: Knowledge reasoning over knowledge graph","volume":"141","author":"Chen","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.knosys.2026.116161_b9","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.112454","article-title":"A survey on temporal knowledge graph embedding: Models and applications","volume":"304","author":"Zhang","year":"2024","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.knosys.2026.116161_b10","series-title":"Flow-modulated scoring for semantic-aware knowledge graph completion","author":"Li","year":"2025"},{"key":"10.1016\/j.knosys.2026.116161_b11","series-title":"Evolving beyond snapshots: Harmonizing structure and sequence via entity state tuning for temporal knowledge graph forecasting","author":"Li","year":"2026"},{"key":"10.1016\/j.knosys.2026.116161_b12","series-title":"Learning deep transformer models for machine translation","author":"Wang","year":"2019"},{"key":"10.1016\/j.knosys.2026.116161_b13","doi-asserted-by":"crossref","unstructured":"Xiao Long, Liansheng Zhuang, Aodi Li, Houqiang Li, Shafei Wang, Fact Embedding through Diffusion Model for Knowledge Graph Completion, in: Proceedings of the ACM on Web Conference 2024, 2024, pp. 2020\u20132029.","DOI":"10.1145\/3589334.3645451"},{"key":"10.1016\/j.knosys.2026.116161_b14","doi-asserted-by":"crossref","unstructured":"Hongwei Wang, Hongyu Ren, Jure Leskovec, Relational message passing for knowledge graph completion, in: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2021, pp. 1697\u20131707.","DOI":"10.1145\/3447548.3467247"},{"key":"10.1016\/j.knosys.2026.116161_b15","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 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023, pp. 3446\u20133457.","DOI":"10.1145\/3580305.3599404"},{"key":"10.1016\/j.knosys.2026.116161_b16","doi-asserted-by":"crossref","unstructured":"Qinggang Zhang, Keyu Duan, Junnan Dong, Pai Zheng, Xiao Huang, Logical reasoning with relation network for inductive knowledge graph completion, in: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024, pp. 4268\u20134277.","DOI":"10.1145\/3637528.3671911"},{"key":"10.1016\/j.knosys.2026.116161_b17","series-title":"International Conference on Neural Information Processing","first-page":"567","article-title":"Context-driven knowledge graph completion with semantic-aware relational message passing","author":"Li","year":"2025"},{"key":"10.1016\/j.knosys.2026.116161_b18","unstructured":"Aleksandar Pavlovi\u0107, Emanuel Sallinger, ExpressivE: A Spatio-Functional Embedding For Knowledge Graph Completion, in: International Conference on Learning Representations, 2023."},{"key":"10.1016\/j.knosys.2026.116161_b19","series-title":"2024 International Joint Conference on Neural Networks","first-page":"1","article-title":"A diffusion model for inductive knowledge graph completion","author":"Chen","year":"2024"},{"issue":"8","key":"10.1016\/j.knosys.2026.116161_b20","first-page":"8850","article-title":"KGDM: A diffusion model to capture multiple relation semantics for knowledge graph embedding","volume":"38","author":"Long","year":"2024","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"10.1016\/j.knosys.2026.116161_b21","article-title":"Translating embeddings for modeling multi-relational data","volume":"26","author":"Bordes","year":"2013","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.knosys.2026.116161_b22","unstructured":"Bishan Yang, Scott Wen-tau Yih, Xiaodong He, Jianfeng Gao, Li Deng, Embedding Entities and Relations for Learning and Inference in Knowledge Bases, in: Proceedings of the International Conference on Learning Representations (ICLR) 2015, 2015."},{"key":"10.1016\/j.knosys.2026.116161_b23","article-title":"Learning entity and relation embeddings for knowledge graph completion","volume":"vpl. 29","author":"Lin","year":"2015"},{"key":"10.1016\/j.knosys.2026.116161_b24","series-title":"International Conference on Machine Learning","first-page":"2071","article-title":"Complex embeddings for simple link prediction","author":"Trouillon","year":"2016"},{"key":"10.1016\/j.knosys.2026.116161_b25","series-title":"RotatE: Knowledge graph embedding by relational rotation in complex space","author":"Sun","year":"2019"},{"key":"10.1016\/j.knosys.2026.116161_b26","unstructured":"Tengwei Song, Jie Luo, Lei Huang, Rot-Pro: Modeling Transitivity by Projection in Knowledge Graph Embedding, in: Proceedings of the Thirty-Fifth Annual Conference on Advances in Neural Information Processing Systems (NeurIPS), 2021."},{"key":"10.1016\/j.knosys.2026.116161_b27","first-page":"3065","article-title":"Learning hierarchy-aware knowledge graph embeddings for link prediction","volume":"vol. 34","author":"Zhang","year":"2020"},{"key":"10.1016\/j.knosys.2026.116161_b28","first-page":"9649","article-title":"Boxe: A box embedding model for knowledge base completion","volume":"33","author":"Abboud","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"4","key":"10.1016\/j.knosys.2026.116161_b29","doi-asserted-by":"crossref","first-page":"1902","DOI":"10.1109\/TKDE.2025.3531372","article-title":"Hycube: Efficient knowledge hypergraph 3D circular convolutional embedding","volume":"37","author":"Li","year":"2025","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.knosys.2026.116161_b30","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.113288","article-title":"Knowledge-based natural answer generation via effective graph learning","volume":"316","author":"Liu","year":"2025","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.knosys.2026.116161_b31","series-title":"Proceedings of the 41st International Conference on Machine Learning","article-title":"Knowledge graphs can be learned with just intersection features","author":"Le","year":"2024"},{"key":"10.1016\/j.knosys.2026.116161_b32","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.113500","article-title":"KERMIT: Knowledge graph completion of enhanced relation modeling with inverse transformation","volume":"324","author":"Li","year":"2025","journal-title":"Knowl.-Based Syst.","ISSN":"https:\/\/id.crossref.org\/issn\/0950-7051","issn-type":"print"},{"key":"10.1016\/j.knosys.2026.116161_b33","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume":"33","author":"Ho","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.knosys.2026.116161_b34","series-title":"International Conference on Machine Learning","first-page":"2256","article-title":"Deep unsupervised learning using nonequilibrium thermodynamics","author":"Sohl-Dickstein","year":"2015"},{"key":"10.1016\/j.knosys.2026.116161_b35","article-title":"Visual grounding in 2D and 3D: A unified perspective and survey","author":"Guo","year":"2025","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.knosys.2026.116161_b36","doi-asserted-by":"crossref","unstructured":"Guotao Liang, Juncheng Hu, Ximing Xing, Jing Zhang, Qian Yu, Multi-object sketch animation with grouping and motion trajectory priors, in: Proceedings of the 33rd ACM International Conference on Multimedia, 2025, pp. 9237\u20139246.","DOI":"10.1145\/3746027.3754502"},{"key":"10.1016\/j.knosys.2026.116161_b37","first-page":"8780","article-title":"Diffusion models beat gans on image synthesis","volume":"34","author":"Dhariwal","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.knosys.2026.116161_b38","doi-asserted-by":"crossref","unstructured":"Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, Bj\u00f6rn Ommer, High-resolution image synthesis with latent diffusion models, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 10684\u201310695.","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"10.1016\/j.knosys.2026.116161_b39","unstructured":"Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, Matthew Le, Flow Matching for Generative Modeling, in: The Eleventh International Conference on Learning Representations, 2023."},{"key":"10.1016\/j.knosys.2026.116161_b40","unstructured":"Yang Song, Prafulla Dhariwal, Mark Chen, Ilya Sutskever, Consistency models, in: Proceedings of the 40th International Conference on Machine Learning, 2023, pp. 32211\u201332252."},{"key":"10.1016\/j.knosys.2026.116161_b41","first-page":"17981","article-title":"Structured denoising diffusion models in discrete state-spaces","volume":"34","author":"Austin","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.knosys.2026.116161_b42","first-page":"4328","article-title":"Diffusion-lm improves controllable text generation","volume":"35","author":"Li","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.knosys.2026.116161_b43","series-title":"DiffuSeq: Sequence to sequence text generation with diffusion models","author":"Gong","year":"2023"},{"key":"10.1016\/j.knosys.2026.116161_b44","article-title":"Attention is all you need","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.knosys.2026.116161_b45","series-title":"International Conference on Machine Learning","first-page":"1263","article-title":"Neural message passing for quantum chemistry","author":"Gilmer","year":"2017"},{"key":"10.1016\/j.knosys.2026.116161_b46","series-title":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","isbn-type":"print","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1145\/3637528.3671997","article-title":"Diffusione: Reasoning on knowledge graphs via diffusion-based graph neural networks","author":"Cao","year":"2024","ISBN":"https:\/\/id.crossref.org\/isbn\/9798400704901"},{"key":"10.1016\/j.knosys.2026.116161_b47","doi-asserted-by":"crossref","unstructured":"William Peebles, Saining Xie, Scalable diffusion models with transformers, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2023, pp. 4195\u20134205.","DOI":"10.1109\/ICCV51070.2023.00387"},{"key":"10.1016\/j.knosys.2026.116161_b48","doi-asserted-by":"crossref","unstructured":"Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, Deep residual learning for image recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"10.1016\/j.knosys.2026.116161_b49","article-title":"Distributed representations of words and phrases and their compositionality","volume":"26","author":"Mikolov","year":"2013","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.knosys.2026.116161_b50","series-title":"Proceedings of the 3rd Workshop on Continuous Vector Space Models and their Compositionality","first-page":"57","article-title":"Observed versus latent features for knowledge base and text inference","author":"Toutanova","year":"2015"},{"key":"10.1016\/j.knosys.2026.116161_b51","article-title":"Convolutional 2d knowledge graph embeddings","volume":"vol. 32","author":"Dettmers","year":"2018"},{"key":"10.1016\/j.knosys.2026.116161_b52","series-title":"Kinship","author":"Hinton","year":"1986"},{"issue":"suppl_1","key":"10.1016\/j.knosys.2026.116161_b53","doi-asserted-by":"crossref","first-page":"D267","DOI":"10.1093\/nar\/gkh061","article-title":"The unified medical language system (UMLS): integrating biomedical terminology","volume":"32","author":"Bodenreider","year":"2004","journal-title":"\u201dNucleic Acids Research\u201d"},{"issue":"12","key":"10.1016\/j.knosys.2026.116161_b54","doi-asserted-by":"crossref","first-page":"13002","DOI":"10.1109\/TKDE.2023.3272568","article-title":"TDN: Triplet distributor network for knowledge graph completion","volume":"35","author":"Wang","year":"2023","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.knosys.2026.116161_b55","unstructured":"Meng Qu, Junkun Chen, Louis-Pascal Xhonneux, Yoshua Bengio, Jian Tang, RNNLogic: Learning Logic Rules for Reasoning on Knowledge Graphs."},{"key":"10.1016\/j.knosys.2026.116161_b56","article-title":"Drum: End-to-end differentiable rule mining on knowledge graphs","volume":"32","author":"Sadeghian","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.knosys.2026.116161_b57","series-title":"Composition-based multi-relational graph convolutional networks","author":"Vashishth","year":"2020"},{"key":"10.1016\/j.knosys.2026.116161_b58","first-page":"29476","article-title":"Neural bellman-ford networks: A general graph neural network framework for link prediction","volume":"34","author":"Zhu","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.knosys.2026.116161_b59","doi-asserted-by":"crossref","DOI":"10.1016\/j.ins.2024.121639","article-title":"Aggregation or separation? Adaptive embedding message passing for knowledge graph completion","volume":"691","author":"Li","year":"2025","journal-title":"Inform. Sci.","ISSN":"https:\/\/id.crossref.org\/issn\/0020-0255","issn-type":"print"},{"key":"10.1016\/j.knosys.2026.116161_b60","article-title":"Knowledge graph embedding by translating on hyperplanes","volume":"vol. 28","author":"Wang","year":"2014"},{"key":"10.1016\/j.knosys.2026.116161_b61","series-title":"International Conference on Machine Learning","first-page":"9448","article-title":"Inductive relation prediction by subgraph reasoning","author":"Teru","year":"2020"},{"key":"10.1016\/j.knosys.2026.116161_b62","doi-asserted-by":"crossref","unstructured":"Yongqi Zhang, Quanming Yao, Knowledge graph reasoning with relational digraph, in: Proceedings of the ACM Web Conference 2022, 2022, pp. 912\u2013924.","DOI":"10.1145\/3485447.3512008"}],"container-title":["Knowledge-Based Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126008877?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126008877?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T01:14:21Z","timestamp":1780017261000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0950705126008877"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":62,"alternative-id":["S0950705126008877"],"URL":"https:\/\/doi.org\/10.1016\/j.knosys.2026.116161","relation":{},"ISSN":["0950-7051"],"issn-type":[{"value":"0950-7051","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Multi-Condition Latent Diffusion Network for Semantic-aware 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.2026.116161","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"116161"}}