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Distance based knowledge graph embedding methods show promising results on link prediction task, on which two topics have been widely studied: one is the ability to handle complex relations, such as N-to-1, 1-to-N and N-to-N, the other is to encode various relation patterns, such as symmetry/antisymmetry.
Rotate: Knowledge graph embedding by relational rotation in complex space
Zhiqing Sun, Zhi-Hong Deng, Jian-Yun Nie, and Jian Tang. 2019 · 1902
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Quaternion knowledge graph embedding
Shuai Zhang, Yi Tay, Lina Yao, and Qi Liu. 2019 · 1904
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Orthogonal relation transforms with graph context modeling for knowledge graph embedding
Yun Tang, Jing Huang, Guangtao Wang, Xiaodong He, and Bowen Zhou. 2019 · 1911
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George A Miller. 1995 · 1995
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Yago: a core of semantic knowledge
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Translating embeddings for modeling multi-relational data
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Knowledge graph embedding by translating on hyperplanes
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Holographic embeddings of knowledge graphs
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Knowledge base completion: Baselines strike back
Rudolf Kadlec, Ondrej Bajgar, and Jan Kleindienst. 2017 · 2017
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Convolutional 2d knowledge graph embeddings
Tim Dettmers, Pasquale Minervini, Pontus Stenetorp, and Sebastian Riedel. 2018 · 2018
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Improving knowledge graph embedding using simple constraints
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Knowledge graph embedding with iterative guidance from soft rules
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Dbpedia–a large-scale, multilingual knowledge base extracted from wikipedia
Jens Lehmann, Robert Isele, Max Jakob, Anja Jentzsch, Dimitris Kontokostas, Pablo N Mendes, Sebastian Hellmann, Mohamed Morsey, Patrick Van Kleef, Sören Auer, et al. 2015 · 2015
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Learning entity and relation embeddings for knowledge graph completion
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Observed versus latent features for knowledge base and text inference
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Simple embedding for link prediction in knowledge graphs
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Never-ending learning
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On multi-relational link prediction with bilinear models
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Improved knowledge graph embedding using background taxonomic information
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Probabilistic logic neural networks for reasoning
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Complex embeddings for simple link prediction
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