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The challenge of answering graph queries over incomplete knowledge graphs is gaining significant attention in the machine learning community.
Optimal implementation of conjunctive queries in relational data bases
Ashok K. Chandra and Philip M. Merlin · 1977
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Nontraditional applications of automata theory
Moshe Y Vardi · 1994
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Unql: a query language and algebra for semistructured data based on structural recursion
Peter Buneman, Mary Fernandez, and Dan Suciu · 2000
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Message passing query embedding
Daniel Daza and Michael Cochez · 2002
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The complexity of temporal logic model checking
Philippe Schnoebelen · 2002
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The dichotomy of conjunctive queries on probabilistic structures
Nilesh N. Dalvi and Dan Suciu · 2007
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Probabilistic Databases
Dan Suciu, Dan Olteanu, Christopher Ré, and Christoph Koch · 2011
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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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The dichotomy of probabilistic inference for unions of conjunctive queries
Nilesh Dalvi and Dan Suciu · 2013
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Skew strikes back: new developments in the theory of join algorithms
Hung Q. Ngo, Christopher Ré, and Atri Rudra · 2013
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Efficient approximations of conjunctive queries
Pablo Barceló, Leonid Libkin, and Miguel Romero · 2014
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A review of relational machine learning for knowledge graphs
Maximilian Nickel, Kevin Murphy, Volker Tresp, and Evgeniy Gabrilovich · 2015
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Observed versus latent features for knowledge base and text inference
Kristina Toutanova and Danqi Chen · 2015
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Embedding entities and relations for learning and inference in knowledge bases
Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng · 2015
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Complex embeddings for simple link prediction
Théo Trouillon, Johannes Welbl, Sebastian Riedel, Éric Gaussier, and Guillaume Bouchard · 2016
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Foundations of modern query languages for graph databases
Renzo Angles, Marcelo Arenas, Pablo Barceló, Aidan Hogan, Juan Reutter, and Domagoj Vrgoč · 2017
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Dissociation and propagation for approximate lifted inference with standard relational database management systems
Wolfgang Gatterbauer and Dan Suciu · 2017
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DeepPath: A reinforcement learning method for knowledge graph reasoning
Wenhan Xiong, Thien Hoang, and William Yang Wang · 2017
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Embedding logical queries on knowledge graphs
William L. Hamilton, Payal Bajaj, Marinka Zitnik, Dan Jurafsky, and Jure Leskovec · 2018
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Modeling relational data with graph convolutional networks
Michael Sejr Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, and Max Welling · 2018
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Query2box: Reasoning over knowledge graphs in vector space using box embeddings
Cone: Cone embeddings for multi-hop reasoning over knowledge graphs
Zhanqiu Zhang, Jie Wang, Jiajun Chen, Shuiwang Ji, and Feng Wu · 2021
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Neural Bellman-Ford Networks: A general graph neural network framework for link prediction
Zhaocheng Zhu, Zuobai Zhang, Louis-Pascal Xhonneux, and Jian Tang · 2021
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A survey of RDF stores & SPARQL engines for querying knowledge graphs
Waqas Ali, Muhammad Saleem, Bin Yao, Aidan Hogan, and Axel-Cyrille Ngonga Ngomo · 2022
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Query embedding on hyper-relational knowledge graphs
Dimitrios Alivanistos, Max Berrendorf, Michael Cochez, and Mikhail Galkin · 2022
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Fuzzy logic based logical query answering on knowledge graphs
Xuelu Chen, Ziniu Hu, and Yizhou Sun · 2022
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Inductive logical query answering in knowledge graphs
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Hongyu Ren, Weihua Hu, and Jure Leskovec · 2019
Cited alongside, same era.
Rotate: Knowledge graph embedding by relational rotation in complex space
Zhiqing Sun, Zhi-Hong Deng, Jian-Yun Nie, and Jian Tang · 2019
Cited alongside, same era.
A more general theory of static approximations for conjunctive queries
Pablo Barceló, Miguel Romero, and Thomas Zeume · 2020
Cited alongside, same era.
Knowledge Graphs - Methodology, Tools and Selected Use Cases
Dieter Fensel, Umutcan Şimşek, Kevin Angele, Elwin Huaman, Elias Kärle, Oleksandra Panasiuk, Ioan Toma, Jürgen Umbrich, and Alexander Wahler · 2020
Cited alongside, same era.
Beta embeddings for multi-hop logical reasoning in knowledge graphs
Hongyu Ren and Jure Leskovec · 2020
Cited alongside, same era.
Inductive relation prediction by subgraph reasoning
Komal K. Teru, Etienne G. Denis, and William L. Hamilton · 2020
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Composition-based multi-relational graph convolutional networks
Shikhar Vashishth, Soumya Sanyal, Vikram Nitin, and Partha P. Talukdar · 2020
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Michael Galkin, Zhaocheng Zhu, Hongyu Ren, and Jian Tang · 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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GNNQ: A neuro-symbolic approach to query answering over incomplete knowledge graphs
Maximilian Pflueger, David J Tena Cucala, and Egor V Kostylev · 2022
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A generalized weisfeiler-lehman graph kernel
Till Hendrik Schulz, Tamás Horváth, Pascal Welke, and Stefan Wrobel · 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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Adapting neural link predictors for data-efficient complex query answering
Erik Arakelyan, Pasquale Minervini, Daniel Daza, Michael Cochez, and Isabelle Augenstein · 2023
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Answering complex logical queries on knowledge graphs via query computation tree optimization
Yushi Bai, Xin Lv, Juanzi Li, and Lei Hou · 2023
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NQE: N-ary query embedding for complex query answering over hyper-relational knowledge graphs
Haoran Luo, E Haihong, Yuhao Yang, Gengxian Zhou, Yikai Guo, Tianyu Yao, Zichen Tang, Xueyuan Lin, and Kaiyang Wan · 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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Rethinking existential first order queries and their inference on knowledge graphs, 2023
Hang Yin, Zihao Wang, and Yangqiu Song · 2023
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