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Reasoning on knowledge graphs is a challenging task because it utilizes observed information to predict the missing one.
Optimal implementation of conjunctive queries in relational data bases
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Wikidata: a free collaborative knowledgebase
Denny Vrandečić and Markus Krötzsch · 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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TensorLog: A Differentiable Deductive Database, July 2016
William W. Cohen · 2016
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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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Will Hamilton, Payal Bajaj, Marinka Zitnik, Dan Jurafsky, and Jure Leskovec · 2018
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Canonical Tensor Decomposition for Knowledge Base Completion
Timothée Lacroix, Nicolas Usunier, and Guillaume Obozinski · 2018
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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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Query2Particles: Knowledge Graph Reasoning with Particle Embeddings
Jiaxin Bai, Zihao Wang, Hongming Zhang, and Yangqiu Song · 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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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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Complex Query Answering with Neural Link Predictors (Extended Abstract)*
Pasquale Minervini, Erik Arakelyan, Daniel Daza, and Michael Cochez · 2022
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Analyzing differentiable fuzzy logic operators
Emile van Krieken, Erman Acar, and Frank van Harmelen · 2022
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H Ren, W Hu, and J Leskovec · 2020
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Logical Queries on Knowledge Graphs: Emerging Interface of Incomplete Relational Data
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GammaE: Gamma Embeddings for Logical Queries on Knowledge Graphs, October 2022
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Answering Complex Logical Queries on Knowledge Graphs via Query Computation Tree Optimization
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