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Knowledge graph embedding aims to learn distributed representations for entities and relations, and is proven to be effective in many applications.
Dropout: A Simple Way to Prevent Neural Networks from Overfitting
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Yago: a core of semantic knowledge
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Automatically acquiring a semantic network of related concepts
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Learning Structured Embeddings of Knowledge Bases
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Random Walk Inference and Learning in A Large Scale Knowledge Base. In EMNLP . ACL, 529–539
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A latent factor model for highly multi-relational data
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Translating Embeddings for Modeling Multi-relational Data
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Reasoning With Neural Tensor Networks for Knowledge Base Completion
Richard Socher, Danqi Chen, Christopher D. Manning, and Andrew Y. Ng. 2013 · 2013
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A semantic matching energy function for learning with multi-relational data - Application to word-sense disambiguation
Antoine Bordes, Xavier Glorot, Jason Weston, and Yoshua Bengio. 2014a · 2014
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A semantic matching energy function for learning with multi-relational data - Application to word-sense disambiguation
Antoine Bordes, Xavier Glorot, Jason Weston, and Yoshua Bengio. 2014b · 2014
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Adam: A Method for Stochastic Optimization
Diederik P. Kingma and Jimmy Ba. 2014 · 2014
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Knowledge Graph Embedding by Translating on Hyperplanes
Zhen Wang, Jianwen Zhang, Jianlin Feng, and Zheng Chen. 2014 · 2014
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Fast rule mining in ontological knowledge bases with AMIE+
Luis Galárraga, Christina Teflioudi, Katja Hose, and Fabian M. Suchanek. 2015 · 2015
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Composing Relationships with Translations
Alberto García-Durán, Antoine Bordes, and Nicolas Usunier. 2015 · 2015
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STransE: a novel embedding model of entities and relationships in knowledge bases. In HLT-NAACL . The Association for Computational Linguistics, 460–466
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A Review of Relational Machine Learning for Knowledge Graphs
Maximilian Nickel, Kevin Murphy, Volker Tresp, and Evgeniy Gabrilovich. 2016a · 2016
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RDF2Vec: RDF Graph Embeddings for Data Mining
Petar Ristoski and Heiko Paulheim. 2016 · 2016
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TransG : A Generative Model for Knowledge Graph Embedding
Han Xiao, Minlie Huang, and Xiaoyan Zhu. 2016 · 2016
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Representation Learning of Knowledge Graphs with Hierarchical Types
Ruobing Xie, Zhiyuan Liu, and Maosong Sun. 2016 · 2016
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Neural Generative Question Answering. In Proceedings of IJCAI
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Shizhu He, Kang Liu, Guoliang Ji, and Jun Zhao. 2015 · 2015
Cited alongside, same era.
Knowledge Graph Embedding via Dynamic Mapping Matrix
Guoliang Ji, Shizhu He, Liheng Xu, Kang Liu, and Jun Zhao. 2015 · 2015
Cited alongside, same era.
Type-Constrained Representation Learning in Knowledge Graphs
Denis Krompaß, Stephan Baier, and Volker Tresp. 2015 · 2015
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Modeling Relation Paths for Representation Learning of Knowledge Bases
Yankai Lin, Zhiyuan Liu, Huan-Bo Luan, Maosong Sun, Siwei Rao, and Song Liu. 2015a · 2015
Cited alongside, same era.
Learning Entity and Relation Embeddings for Knowledge Graph Completion
Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, and Xuan Zhu. 2015b · 2015
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Compositional Vector Space Models for Knowledge Base Completion
Arvind Neelakantan, Benjamin Roth, and Andrew McCallum. 2015 · 2015
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Observed versus latent features for knowledge base and text inference. In Proceedings of the 3rd Workshop on Continuous Vector Space Models and their Compositionality . 57–66
Kristina Toutanova and Danqi Chen. 2015 · 2015
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Jun Yin, Xin Jiang, Zhengdong Lu, Lifeng Shang, Hang Li, and Xiaoming Li. 2016 · 2016
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Scalable and Interpretable Product Recommendations via Overlapping Co-Clustering
Reinhard Heckel, Michail Vlachos, Thomas P. Parnell, and Celestine Dünner. 2017 · 2017
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Analogical Inference for Multi-relational Embeddings. In Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6-11 August 2017 . 2168–2178
Hanxiao Liu, Yuexin Wu, and Yiming Yang. 2017 · 2017
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ProjE: Embedding Projection for Knowledge Graph Completion
Baoxu Shi and Tim Weninger. 2017 · 2017
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Differentiable Learning of Logical Rules for Knowledge Base Reasoning. In NIPS . 2316–2325
Fan Yang, Zhilin Yang, and William W. Cohen. 2017 · 2017
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Knowledge Graph Embedding with Diversity of Structures
Wen Zhang. 2017 · 2017
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Modeling Relational Data with Graph Convolutional Networks
Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, and Max Welling. 2018 · 2018
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TEM: Tree-enhanced Embedding Model for Explainable Recommendation. In Proceedings of WWW . 1543–1552
Xiang Wang, Xiangnan He, Fuli Feng, Liqiang Nie, and Tat-Seng Chua. 2018 · 2018
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Complex Embeddings for Simple Link Prediction
Théo Trouillon, Johannes Welbl, Sebastian Riedel, Éric Gaussier, and Guillaume Bouchard. 2016 · 2080
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