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The increasing demand for deep learning-based foundation models has highlighted the importance of efficient data retrieval mechanisms.
Public-key cryptosystems based on composite degree residuosity classes
Pascal Paillier · 1999
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Differential privacy
Cynthia Dwork · 2006
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From federated databases to a federated data warehouse system
Stefan Berger and Michael Schrefl · 2008
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Freebase: a collaboratively created graph database for structuring human knowledge
Kurt Bollacker, Colin Evans, Praveen Paritosh, Tim Sturge, and Jamie Taylor · 2008
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Differential privacy: A survey of results
Cynthia Dwork · 2008
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Toward an architecture for never-ending language learning
Andrew Carlson, Justin Betteridge, Bryan Kisiel, Burr Settles, Estevam Hruschka, and Tom Mitchell · 2010
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Translating embeddings for modeling multi-relational data
Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko · 2013
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Amie: association rule mining under incomplete evidence in ontological knowledge bases
Luis Antonio Galárraga, Christina Teflioudi, Katja Hose, and Fabian Suchanek · 2013
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A federated query answering system for semantic web data
Yingjie Li · 2013
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The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
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Fraud detection: Discovering connections with graph databases
Gorka Sadowski and Philip Rathle · 2014
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Observed versus latent features for knowledge base and text inference
Kristina Toutanova and Danqi Chen · 2015
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Knowledge base completion using embeddings and rules
Quan Wang, Bin Wang, and Li Guo · 2015
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Smcql: secure querying for federated databases
Johes Bater, Gregory Elliott, Craig Eggen, Satyender Goel, Abel Kho, and Jennie Rogers · 2017
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Differentially private federated learning: A client level perspective
Robin C Geyer, Tassilo Klein, and Moin Nabi · 2017
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Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
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Secureml: A system for scalable privacy-preserving machine learning
Payman Mohassel and Yupeng Zhang · 2017
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Embedding logical queries on knowledge graphs
Will Hamilton, Payal Bajaj, Marinka Zitnik, Dan Jurafsky, and Jure Leskovec · 2018
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Deriving validity time in knowledge graph
Julien Leblay and Melisachew Wudage Chekol · 2018
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2018
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Unifying knowledge graph learning and recommendation: Towards a better understanding of user preferences
Yixin Cao, Xiang Wang, Xiangnan He, Zikun Hu, and Tat-Seng Chua · 2019
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Kgat: Knowledge graph attention network for recommendation
Xiang Wang, Xiangnan He, Yixin Cao, Meng Liu, and Tat-Seng Chua · 2019
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Federated machine learning: Concept and applications
Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong · 2019
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Deep leakage from gradients
Ligeng Zhu, Zhijian Liu, and Song Han · 2019
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Complex query answering with neural link predictors
Erik Arakelyan, Daniel Daza, Pasquale Minervini, and Michael Cochez · 2020
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Differentially private secure multi-party computation for federated learning in financial applications
David Byrd and Antigoni Polychroniadou · 2020
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Quantifying privacy leakage in graph embedding
Vasisht Duddu, Antoine Boutet, and Virat Shejwalkar · 2020
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Retrieval-augmented generation for knowledge-intensive nlp tasks
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al · 2020
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Federated learning for open banking
Guodong Long, Yue Tan, Jing Jiang, and Chengqi Zhang · 2020
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Query2box: Reasoning over knowledge graphs in vector space using box embeddings
New directions in cryptography
Whitfield Diffie and Martin E Hellman · 2022
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A federated multi-server knowledge graph embedding framework for link prediction
Ce Hu, Baisong Liu, Xueyuan Zhang, Zhiye Wang, Chennan Lin, and Linze Luo · 2022
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Learning privacy-preserving graph convolutional network with partially observed sensitive attributes
Hui Hu, Lu Cheng, Jayden Parker Vap, and Mike Borowczak · 2022
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Fedcke: Cross-domain knowledge graph embedding in federated learning
Wei Huang, Jia Liu, Tianrui Li, Shenggong Ji, Dexian Wang, and Tianqiang Huang · 2022
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Mask and reason: Pre-training knowledge graph transformers for complex logical queries
Xiao Liu, Shiyu Zhao, Kai Su, Yukuo Cen, Jiezhong Qiu, Mengdi Zhang, Wei Wu, Yuxiao Dong, and Jie Tang · 2022
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H Ren, W Hu, and J Leskovec · 2020
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Beta embeddings for multi-hop logical reasoning in knowledge graphs
Hongyu Ren and Jure Leskovec · 2020
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Federated recommendation systems
Liu Yang, Ben Tan, Vincent W Zheng, Kai Chen, and Qiang Yang · 2020
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{ \{ BatchCrypt } \} : Efficient homomorphic encryption for { \{ Cross-Silo } \} federated learning
Chengliang Zhang, Suyi Li, Junzhe Xia, Wei Wang, Feng Yan, and Yang Liu · 2020
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Query embedding on hyper-relational knowledge graphs
Dimitrios Alivanistos, Max Berrendorf, Michael Cochez, and Mikhail Galkin · 2021
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Fede: Embedding knowledge graphs in federated setting
Mingyang Chen, Wen Zhang, Zonggang Yuan, Yantao Jia, and Huajun Chen · 2021
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Probabilistic entity representation model for reasoning over knowledge graphs
Nurendra Choudhary, Nikhil Rao, Sumeet Katariya, Karthik Subbian, and Chandan Reddy · 2021
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Zihao Wang, Yangqiu Song, Ginny Wong, and Simon See · 2022
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Reasoning over multi-view knowledge graphs
Zhaohan Xi, Ren Pang, Changjiang Li, Tianyu Du, Shouling Ji, Fenglong Ma, and Ting Wang · 2022
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Gammae: Gamma embeddings for logical queries on knowledge graphs
Dong Yang, Peijun Qing, Yang Li, Haonan Lu, and Xiaodong Lin · 2022
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Efficient federated learning on knowledge graphs via privacy-preserving relation embedding aggregation
Kai Zhang, Yu Wang, Hongyi Wang, Lifu Huang, Carl Yang, Xun Chen, and Lichao Sun · 2022
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Neural-symbolic models for logical queries on knowledge graphs
Zhaocheng Zhu, Mikhail Galkin, Zuobai Zhang, and Jian Tang · 2022
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Retrieval-augmented generation for large language models: A survey
Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, and Haofen Wang · 2023
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Independent distribution regularization for private graph embedding
Qi Hu and Yangqiu Song · 2023
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Quantifying and defending against privacy threats on federated knowledge graph embedding
Yuke Hu, Wei Liang, Ruofan Wu, Kai Xiao, Weiqiang Wang, Xiaochen Li, Jinfei Liu, and Zhan Qin · 2023
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Neural graph reasoning: Complex logical query answering meets graph databases
Hongyu Ren, Mikhail Galkin, Michael Cochez, Zhaocheng Zhu, and Jure Leskovec · 2023
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Fedmkgc: Privacy-preserving federated multilingual knowledge graph completion
Wei Tang, Zhiqian Wu, Yixin Cao, Yong Liao, and Pengyuan Zhou · 2023
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Federated knowledge graph completion via latent embedding sharing and tensor factorization
Maolin Wang, Dun Zeng, Zenglin Xu, Ruocheng Guo, and Xiangyu Zhao · 2023
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User consented federated recommender system against personalized attribute inference attack
Qi Hu and Yangqiu Song · 2024
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Privacy-preserved neural graph databases
Qi Hu, Haoran Li, Jiaxin Bai, Zihao Wang, and Yangqiu Song · 2024
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Health-llm: Personalized retrieval-augmented disease prediction system
Mingyu Jin, Qinkai Yu, Dong Shu, Chong Zhang, Lizhou Fan, Wenyue Hua, Suiyuan Zhu, Yanda Meng, Zhenting Wang, Mengnan Du, et al · 2024
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Kragen: a knowledge graph-enhanced rag framework for biomedical problem solving using large language models
Nicholas Matsumoto, Jay Moran, Hyunjun Choi, Miguel E Hernandez, Mythreye Venkatesan, Paul Wang, and Jason H Moore · 2024
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Text-attributed graph representation learning: Methods, applications, and challenges
Delvin Ce Zhang, Menglin Yang, Rex Ying, and Hady W Lauw · 2024
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Top ten challenges towards agentic neural graph databases
Jiaxin Bai, Zihao Wang, Yukun Zhou, Hang Yin, Weizhi Fei, Qi Hu, Zheye Deng, Jiayang Cheng, Tianshi Zheng, Hong Ting Tsang, et al · 2025
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Rag-based explainable prediction of road users behaviors for automated driving using knowledge graphs and large language models
Mohamed Manzour Hussien, Angie Nataly Melo, Augusto Luis Ballardini, Carlota Salinas Maldonado, Rubén Izquierdo, and Miguel Angel Sotelo · 2025
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