{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T05:05:04Z","timestamp":1783746304000,"version":"3.55.0"},"reference-count":73,"publisher":"Elsevier BV","issue":"7","license":[{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100013804","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100013804","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["DUT25YG108"],"award-info":[{"award-number":["DUT25YG108"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62276043"],"award-info":[{"award-number":["62276043"]}],"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":["62302076"],"award-info":[{"award-number":["62302076"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Information Processing &amp; Management"],"published-print":{"date-parts":[[2026,11]]},"DOI":"10.1016\/j.ipm.2026.104891","type":"journal-article","created":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T20:39:11Z","timestamp":1779223151000},"page":"104891","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"PB","title":["Memory-KGC: Memory-augmented structural learning for Knowledge Graph Completion"],"prefix":"10.1016","volume":"63","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-8093-9784","authenticated-orcid":false,"given":"Jiru","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6515-134X","authenticated-orcid":false,"given":"Yuanyuan","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-6774-2647","authenticated-orcid":false,"given":"Dinghao","family":"Pan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5141-0259","authenticated-orcid":false,"given":"Ling","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0872-7688","authenticated-orcid":false,"given":"Hongfei","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.ipm.2026.104891_b1","series-title":"Proceedings of the 41st international conference on machine learning","first-page":"2527","article-title":"Memory consolidation enables long-context video understanding","author":"Bala\u017eevi\u0107","year":"2024"},{"key":"10.1016\/j.ipm.2026.104891_b2","doi-asserted-by":"crossref","unstructured":"Bollacker, K., Evans, C., Paritosh, P., Sturge, T., & Taylor, J. (2008). 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).","DOI":"10.1145\/1376616.1376746"},{"key":"10.1016\/j.ipm.2026.104891_b3","article-title":"Translating embeddings for modeling multi-relational data","volume":"26","author":"Bordes","year":"2013","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.ipm.2026.104891_b4","doi-asserted-by":"crossref","unstructured":"Cao, Y., Ji, X., Lv, X., Li, J., Wen, Y., & Zhang, H. (2021). Are Missing Links Predictable? An Inferential Benchmark for Knowledge Graph Completion. In Proceedings of the 59th annual meeting of the association for computational linguistics and the 11th international joint conference on natural language processing (volume 1: long papers) (pp. 6855\u20136865).","DOI":"10.18653\/v1\/2021.acl-long.534"},{"key":"10.1016\/j.ipm.2026.104891_b5","series-title":"International conference on machine learning","first-page":"1597","article-title":"A simple framework for contrastive learning of visual representations","author":"Chen","year":"2020"},{"key":"10.1016\/j.ipm.2026.104891_b6","doi-asserted-by":"crossref","unstructured":"Chen, S., Liu, X., Gao, J., Jiao, J., Zhang, R., & Ji, Y. (2021). HittER: Hierarchical Transformers for Knowledge Graph Embeddings. In Proceedings of the 2021 conference on empirical methods in natural language processing (pp. 10395\u201310407).","DOI":"10.18653\/v1\/2021.emnlp-main.812"},{"key":"10.1016\/j.ipm.2026.104891_b7","unstructured":"Chen, C., Wang, Y., Li, B., & Lam, K.-Y. (2022). Knowledge Is Flat: A Seq2Seq Generative Framework for Various Knowledge Graph Completion. In Proceedings of the 29th international conference on computational linguistics (pp. 4005\u20134017)."},{"key":"10.1016\/j.ipm.2026.104891_b8","unstructured":"de Jong, M., Zemlyanskiy, Y., FitzGerald, N., Sha, F., & Cohen, W. W. Mention Memory: incorporating textual knowledge into Transformers through entity mention attention. In International conference on learning representations."},{"key":"10.1016\/j.ipm.2026.104891_b9","article-title":"Convolutional 2d knowledge graph embeddings","volume":"vol. 32","author":"Dettmers","year":"2018"},{"key":"10.1016\/j.ipm.2026.104891_b10","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al. (2020). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. In International conference on learning representations."},{"key":"10.1016\/j.ipm.2026.104891_b11","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.112804","article-title":"Knowledge graph completion with low-dimensional gated hierarchical hyperbolic embedding","volume":"309","author":"Fang","year":"2025","journal-title":"Knowledge-Based Systems"},{"key":"10.1016\/j.ipm.2026.104891_b12","series-title":"Pro-KGC: Prompt optimization for LLM-based knowledge graph completion","author":"Gader","year":"2025"},{"key":"10.1016\/j.ipm.2026.104891_b13","doi-asserted-by":"crossref","unstructured":"Gao, C., Wang, X., & Sun, J. (2024). TTM-RE: Memory-Augmented Document-Level Relation Extraction. In Proceedings of the 62nd annual meeting of the association for computational linguistics (volume 1: long papers) (pp. 443\u2013458).","DOI":"10.18653\/v1\/2024.acl-long.26"},{"key":"10.1016\/j.ipm.2026.104891_b14","doi-asserted-by":"crossref","unstructured":"Gregucci, C., Nayyeri, M., Hern\u00e1ndez, D., & Staab, S. (2023). Link prediction with attention applied on multiple knowledge graph embedding models. In Proceedings of the ACM web conference 2023 (pp. 2600\u20132610).","DOI":"10.1145\/3543507.3583358"},{"key":"10.1016\/j.ipm.2026.104891_b15","doi-asserted-by":"crossref","first-page":"140509","DOI":"10.52202\/079017-4460","article-title":"MKGL: mastery of a three-word language","volume":"37","author":"Guo","year":"2024","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"2","key":"10.1016\/j.ipm.2026.104891_b16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3703155","article-title":"A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions","volume":"43","author":"Huang","year":"2025","journal-title":"ACM Transactions on Information Systems"},{"key":"10.1016\/j.ipm.2026.104891_b17","doi-asserted-by":"crossref","first-page":"136220","DOI":"10.52202\/079017-4328","article-title":"Kg-fit: Knowledge graph fine-tuning upon open-world knowledge","volume":"37","author":"Jiang","year":"2024","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.ipm.2026.104891_b18","doi-asserted-by":"crossref","unstructured":"Jiang, B., Zhang, Z., Lin, D., Tang, J., & Luo, B. (2019). Semi-supervised learning with graph learning-convolutional networks. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 11313\u201311320).","DOI":"10.1109\/CVPR.2019.01157"},{"issue":"1","key":"10.1016\/j.ipm.2026.104891_b19","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1007\/s10618-022-00891-8","article-title":"Improving embedded knowledge graph multi-hop question answering by introducing relational chain reasoning","volume":"37","author":"Jin","year":"2023","journal-title":"Data Mining and Knowledge Discovery"},{"key":"10.1016\/j.ipm.2026.104891_b20","series-title":"BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension","author":"Lewis","year":"2019"},{"key":"10.1016\/j.ipm.2026.104891_b21","first-page":"5781","article-title":"How does knowledge graph embedding extrapolate to unseen data: a semantic evidence view","volume":"vol. 36","author":"Li","year":"2022"},{"key":"10.1016\/j.ipm.2026.104891_b22","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2024.128614","article-title":"Decoupled semantic graph neural network for knowledge graph embedding","volume":"611","author":"Li","year":"2025","journal-title":"Neurocomputing"},{"key":"10.1016\/j.ipm.2026.104891_b23","series-title":"2024 27th international conference on computer supported cooperative work in design","first-page":"2991","article-title":"RSTIE-KGC: A relation sensitive textual information enhanced knowledge graph completion model","author":"Li","year":"2024"},{"key":"10.1016\/j.ipm.2026.104891_b24","doi-asserted-by":"crossref","unstructured":"Li, D., Tan, Z., Chen, T., & Liu, H. (2024). Contextualization Distillation from Large Language Model for Knowledge Graph Completion. In Findings of the association for computational linguistics: EACL 2024 (pp. 458\u2013477).","DOI":"10.18653\/v1\/2024.findings-eacl.32"},{"key":"10.1016\/j.ipm.2026.104891_b25","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2023.111253","article-title":"Sdformer: A shallow-to-deep feature interaction for knowledge graph embedding","volume":"284","author":"Li","year":"2024","journal-title":"Knowledge-Based Systems"},{"key":"10.1016\/j.ipm.2026.104891_b26","doi-asserted-by":"crossref","unstructured":"Li, J., Yu, H., Luo, X., & Liu, Q. (2024). Cosign: Contextual facts guided generation for knowledge graph completion. In Proceedings of the 2024 conference of the North American chapter of the association for computational linguistics: human language technologies (volume 1: long papers) (pp. 1669\u20131682).","DOI":"10.18653\/v1\/2024.naacl-long.93"},{"issue":"11","key":"10.1016\/j.ipm.2026.104891_b27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3627704","article-title":"Multi-task pre-training language model for semantic network completion","volume":"22","author":"Li","year":"2023","journal-title":"ACM Transactions on Asian and Low-Resource Language Information Processing"},{"issue":"01","key":"10.1016\/j.ipm.2026.104891_b28","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1109\/TKDE.2023.3282989","article-title":"Knowledge graph contrastive learning based on relation-symmetrical structure","volume":"36","author":"Liang","year":"2024","journal-title":"IEEE Transactions on Knowledge & Data Engineering"},{"key":"10.1016\/j.ipm.2026.104891_b29","doi-asserted-by":"crossref","unstructured":"Liu, Y., Cao, Z., Gao, X., Zhang, J., & Yan, R. (2024). Bridging the space gap: Unifying geometry knowledge graph embedding with optimal transport. In Proceedings of the ACM web conference 2024 (pp. 2128\u20132137).","DOI":"10.1145\/3589334.3645565"},{"key":"10.1016\/j.ipm.2026.104891_b30","unstructured":"Liu, J., Mao, Q., Jiang, W., & Li, J. (2024). KNOWFORMER: revisiting transformers for knowledge graph reasoning. In Proceedings of the 41st international conference on machine learning (pp. 31669\u201331690)."},{"key":"10.1016\/j.ipm.2026.104891_b31","series-title":"International semantic web conference","first-page":"199","article-title":"Finetuning generative large language models with discrimination instructions for knowledge graph completion","author":"Liu","year":"2024"},{"key":"10.1016\/j.ipm.2026.104891_b32","article-title":"ISA-kgc: Integrated semantics-structure analysis in knowledge graph completion","author":"Liu","year":"2024","journal-title":"IEEE Access"},{"key":"10.1016\/j.ipm.2026.104891_b33","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.112730","article-title":"Contrastive predictive embedding for learning and inference in knowledge graph","volume":"307","author":"Liu","year":"2025","journal-title":"Knowledge-Based Systems"},{"key":"10.1016\/j.ipm.2026.104891_b34","doi-asserted-by":"crossref","unstructured":"Nandi, A., Kaur, N., Singla, P., et al. (2024). DynaSemble: Dynamic Ensembling of Textual and Structure-Based Models for Knowledge Graph Completion. In Proceedings of the 62nd annual meeting of the association for computational linguistics (volume 2: short papers) (pp. 205\u2013216).","DOI":"10.18653\/v1\/2024.acl-short.20"},{"key":"10.1016\/j.ipm.2026.104891_b35","article-title":"Pytorch: An imperative style, high-performance deep learning library","volume":"32","author":"Paszke","year":"2019","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"4","key":"10.1016\/j.ipm.2026.104891_b36","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2024.103726","article-title":"Explainable knowledge reasoning via thought chains for knowledge-based visual question answering","volume":"61","author":"Qiu","year":"2024","journal-title":"Information Processing & Management"},{"issue":"1","key":"10.1016\/j.ipm.2026.104891_b37","first-page":"205","article-title":"Limits of depth: Over-smoothing and over-squashing in gnns","volume":"7","author":"Qureshi","year":"2023","journal-title":"Big Data Mining and Analytics"},{"issue":"140","key":"10.1016\/j.ipm.2026.104891_b38","first-page":"1","article-title":"Exploring the limits of transfer learning with a unified text-to-text transformer","volume":"21","author":"Raffel","year":"2020","journal-title":"Journal of Machine Learning Research"},{"key":"10.1016\/j.ipm.2026.104891_b39","doi-asserted-by":"crossref","unstructured":"Ryoo, M. S., Gopalakrishnan, K., Kahatapitiya, K., Xiao, T., Rao, K., Stone, A., Lu, Y., Ibarz, J., & Arnab, A. (2023). Token turing machines. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 19070\u201319081).","DOI":"10.1109\/CVPR52729.2023.01828"},{"issue":"4","key":"10.1016\/j.ipm.2026.104891_b40","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2024.103752","article-title":"Knowledge graph representation learning with relation-guided aggregation and interaction","volume":"61","author":"Shang","year":"2024","journal-title":"Information Processing & Management"},{"key":"10.1016\/j.ipm.2026.104891_b41","series-title":"Unifying over-smoothing and over-squashing in graph neural networks: A physics informed approach and beyond","author":"Shao","year":"2023"},{"key":"10.1016\/j.ipm.2026.104891_b42","article-title":"Tgformer: A graph transformer framework for knowledge graph embedding","author":"Shi","year":"2024","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"10.1016\/j.ipm.2026.104891_b43","doi-asserted-by":"crossref","unstructured":"Suchanek, F. M., Kasneci, G., & Weikum, G. (2007). Yago: a core of semantic knowledge. In Proceedings of the 16th international conference on world wide web (pp. 697\u2013706).","DOI":"10.1145\/1242572.1242667"},{"key":"10.1016\/j.ipm.2026.104891_b44","doi-asserted-by":"crossref","unstructured":"Toutanova, K., Chen, D., Pantel, P., Poon, H., Choudhury, P., & Gamon, M. (2015). Representing text for joint embedding of text and knowledge bases. In Proceedings of the 2015 conference on empirical methods in natural language processing (pp. 1499\u20131509).","DOI":"10.18653\/v1\/D15-1174"},{"issue":"5","key":"10.1016\/j.ipm.2026.104891_b45","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2025.104145","article-title":"Knowledge graph validation by integrating LLMs and human-in-the-loop","volume":"62","author":"Tsaneva","year":"2025","journal-title":"Information Processing & Management"},{"key":"10.1016\/j.ipm.2026.104891_b46","unstructured":"Vashishth, S., Sanyal, S., Nitin, V., & Talukdar, P. (2020). Composition-based multi-relational graph convolutional networks. In Proceedings of the 8th international conference on learning representations."},{"issue":"10","key":"10.1016\/j.ipm.2026.104891_b47","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1145\/2629489","article-title":"Wikidata: a free collaborative knowledgebase","volume":"57","author":"Vrande\u010di\u0107","year":"2014","journal-title":"Communications of the ACM"},{"key":"10.1016\/j.ipm.2026.104891_b48","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1162\/tacl_a_00360","article-title":"KEPLER: A unified model for knowledge embedding and pre-trained language representation","volume":"9","author":"Wang","year":"2021","journal-title":"Transactions of the Association for Computational Linguistics"},{"key":"10.1016\/j.ipm.2026.104891_b49","doi-asserted-by":"crossref","unstructured":"Wang, X., He, Q., Liang, J., & Xiao, Y. (2022). Language Models as Knowledge Embeddings. In Proceedings of the 31st international joint conference on artificial intelligence.","DOI":"10.24963\/ijcai.2022\/318"},{"key":"10.1016\/j.ipm.2026.104891_b50","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.112218","article-title":"Trackge: transformer with relation-pattern adaptive contrastive learning for knowledge graph embedding","volume":"301","author":"Wang","year":"2024","journal-title":"Knowledge-Based Systems"},{"key":"10.1016\/j.ipm.2026.104891_b51","doi-asserted-by":"crossref","unstructured":"Wang, H., Ren, H., & Leskovec, J. (2021). Relational message passing for knowledge graph completion. In Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining (pp. 1697\u20131707).","DOI":"10.1145\/3447548.3467247"},{"key":"10.1016\/j.ipm.2026.104891_b52","doi-asserted-by":"crossref","unstructured":"Wang, L., Zhao, W., Wei, Z., & Liu, J. (2022). SimKGC: Simple Contrastive Knowledge Graph Completion with Pre-trained Language Models. In Proceedings of the 60th annual meeting of the association for computational linguistics (volume 1: long papers) (pp. 4281\u20134294).","DOI":"10.18653\/v1\/2022.acl-long.295"},{"key":"10.1016\/j.ipm.2026.104891_b53","doi-asserted-by":"crossref","unstructured":"Wei, Y., Huang, Q., Zhang, Y., & Kwok, J. (2023). KICGPT: Large Language Model with Knowledge in Context for Knowledge Graph Completion. In Findings of the association for computational linguistics: EMNLP 2023 (pp. 8667\u20138683).","DOI":"10.18653\/v1\/2023.findings-emnlp.580"},{"issue":"4","key":"10.1016\/j.ipm.2026.104891_b54","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3639472","article-title":"Enhancing heterogeneous knowledge graph completion with a novel gat-based approach","volume":"18","author":"Wei","year":"2024","journal-title":"ACM Transactions on Knowledge Discovery from Data"},{"key":"10.1016\/j.ipm.2026.104891_b55","doi-asserted-by":"crossref","unstructured":"Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., et al. (2020). Transformers: State-of-the-art natural language processing. In Proceedings of the 2020 conference on empirical methods in natural language processing: system demonstrations (pp. 38\u201345).","DOI":"10.18653\/v1\/2020.emnlp-demos.6"},{"key":"10.1016\/j.ipm.2026.104891_b56","first-page":"5184","article-title":"An efficient memory-augmented transformer for knowledge-intensive nlp tasks","volume":"vol. 2022","author":"Wu","year":"2022"},{"key":"10.1016\/j.ipm.2026.104891_b57","doi-asserted-by":"crossref","first-page":"27387","DOI":"10.52202\/068431-1986","article-title":"Nodeformer: A scalable graph structure learning transformer for node classification","volume":"35","author":"Wu","year":"2022","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.ipm.2026.104891_b58","doi-asserted-by":"crossref","unstructured":"Xie, X., Zhang, N., Li, Z., Deng, S., Chen, H., Xiong, F., Chen, M., & Chen, H. (2022). From discrimination to generation: Knowledge graph completion with generative transformer. In Companion proceedings of the web conference 2022 (pp. 162\u2013165).","DOI":"10.1145\/3487553.3524238"},{"key":"10.1016\/j.ipm.2026.104891_b59","article-title":"One model connects all graphs: Towards training one unified model for multi-domain graph pre-training using adaptive vector quantization","author":"Xie","year":"2025","journal-title":"Information Fusion"},{"key":"10.1016\/j.ipm.2026.104891_b60","doi-asserted-by":"crossref","unstructured":"Xu, D., Zhang, Z., Lin, Z., Wu, X., Zhu, Z., Xu, T., Zhao, X., Zheng, Y., & Chen, E. (2024). Multi-perspective improvement of knowledge graph completion with large language models. In Proceedings of the 2024 joint international conference on computational linguistics, language resources and evaluation (LREC-cOLING 2024) (pp. 11956\u201311968).","DOI":"10.63317\/3vxue5y8m4xy"},{"key":"10.1016\/j.ipm.2026.104891_b61","article-title":"Knowledge graph completion based on a hierarchical graph attention network with structural information","author":"Xu","year":"2025","journal-title":"Knowledge-Based Systems"},{"key":"10.1016\/j.ipm.2026.104891_b62","series-title":"Chatgpt is not enough: Enhancing large language models with knowledge graphs for fact-aware language modeling","author":"Yang","year":"2023"},{"key":"10.1016\/j.ipm.2026.104891_b63","doi-asserted-by":"crossref","unstructured":"Yang, Y., Hernandez Abrego, G., Yuan, S., Guo, M., Shen, Q., Cer, D., Sung, Y.-H., Strope, B., & Kurzweil, R. (2019). Improving Multilingual Sentence Embedding using Bi-directional Dual Encoder with Additive Margin Softmax. In Proceedings of the 28th international joint conference on artificial intelligence.","DOI":"10.24963\/ijcai.2019\/746"},{"key":"10.1016\/j.ipm.2026.104891_b64","unstructured":"Yang, B., Yih, S. W.-t., 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) 2015."},{"key":"10.1016\/j.ipm.2026.104891_b65","series-title":"KG-BERT: BERT for knowledge graph completion","author":"Yao","year":"2019"},{"key":"10.1016\/j.ipm.2026.104891_b66","series-title":"ICASSP 2025-2025 IEEE international conference on acoustics, speech and signal processing","first-page":"1","article-title":"Exploring large language models for knowledge graph completion","author":"Yao","year":"2025"},{"issue":"2","key":"10.1016\/j.ipm.2026.104891_b67","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2025.104460","article-title":"Text-free inductive knowledge graph embedding via meta graph-based prompt learning","volume":"63","author":"Yi","year":"2026","journal-title":"Information Processing & Management"},{"issue":"4","key":"10.1016\/j.ipm.2026.104891_b68","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2025.104156","article-title":"MCCI: A multi-channel collaborative interaction framework for multimodal knowledge graph completion","volume":"62","author":"Zhang","year":"2025","journal-title":"Information Processing & Management"},{"key":"10.1016\/j.ipm.2026.104891_b69","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Liu, X., Zhang, Y., Su, Q., Sun, X., & He, B. (2020). Pretrain-KGE: learning knowledge representation from pretrained language models. In Findings of the association for computational linguistics: EMNLP 2020 (pp. 259\u2013266).","DOI":"10.18653\/v1\/2020.findings-emnlp.25"},{"key":"10.1016\/j.ipm.2026.104891_b70","doi-asserted-by":"crossref","unstructured":"Zhang, H., Zhang, J., & Molybog, I. (2024). Hasa: Hardness and structure-aware contrastive knowledge graph embedding. In Proceedings of the ACM web conference 2024 (pp. 2116\u20132127).","DOI":"10.1145\/3589334.3645564"},{"key":"10.1016\/j.ipm.2026.104891_b71","series-title":"Kgtuner: Efficient hyper-parameter search for knowledge graph learning","author":"Zhang","year":"2022"},{"key":"10.1016\/j.ipm.2026.104891_b72","series-title":"2022 conference on empirical methods in natural language processing, EMNLP 2022","first-page":"5657","article-title":"Training language models with memory augmentation","author":"Zhong","year":"2022"},{"key":"10.1016\/j.ipm.2026.104891_b73","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":"Advances in Neural Information Processing Systems"}],"container-title":["Information Processing &amp; Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0306457326002827?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0306457326002827?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T14:43:52Z","timestamp":1783694632000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0306457326002827"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,11]]},"references-count":73,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2026,11]]}},"alternative-id":["S0306457326002827"],"URL":"https:\/\/doi.org\/10.1016\/j.ipm.2026.104891","relation":{},"ISSN":["0306-4573"],"issn-type":[{"value":"0306-4573","type":"print"}],"subject":[],"published":{"date-parts":[[2026,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Memory-KGC: Memory-augmented structural learning for Knowledge Graph Completion","name":"articletitle","label":"Article Title"},{"value":"Information Processing & Management","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ipm.2026.104891","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"104891"}}