[aC15]Danqi and Chen. Observed versus latent features for knowledge base and text inference. In 3rd Workshop on Continuous Vector Space Models and Their Compositionality. ACL - Association for Computational Linguistics, July 2015. URL:
[ABK+07]Sören Auer, Christian Bizer, Georgi Kobilarov, Jens Lehmann, Richard Cyganiak, and Zachary Ives. Dbpedia: a nucleus for a web of open data. In The semantic web, 722–735. Springer, 2007.
[BB12]James Bergstra and Yoshua Bengio. Random search for hyper-parameter optimization. Journal of Machine Learning Research, 13(Feb):281–305, 2012.
[BHBL11]Christian Bizer, Tom Heath, and Tim Berners-Lee. Linked data: the story so far. In Semantic services, interoperability and web applications: emerging concepts, 205–227. IGI Global, 2011.
[BUGD+13]Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. Translating embeddings for modeling multi-relational data. In Advances in neural information processing systems, 2787–2795. 2013.
[DMSR18]Tim Dettmers, Pasquale Minervini, Pontus Stenetorp, and Sebastian Riedel. Convolutional 2d knowledge graph embeddings. In Procs of AAAI. 2018. URL:
[GB10]Xavier Glorot and Yoshua Bengio. Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the thirteenth international conference on artificial intelligence and statistics, 249–256. 2010.
[HOSM17]Takuo Hamaguchi, Hidekazu Oiwa, Masashi Shimbo, and Yuji Matsumoto. Knowledge transfer for out-of-knowledge-base entities: A graph neural network approach. IJCAI International Joint Conference on Artificial Intelligence, pages 1802–1808, 2017.
[HS17]Katsuhiko Hayashi and Masashi Shimbo. On the equivalence of holographic and complex embeddings for link prediction. CoRR, 2017. URL:, arXiv:1702.05563.
[KBK17]Rudolf Kadlec, Ondrej Bajgar, and Jan Kleindienst. Knowledge base completion: baselines strike back. CoRR, 2017. URL:, arXiv:1705.10744.
[LUO18]Timothee Lacroix, Nicolas Usunier, and Guillaume Obozinski. Canonical tensor decomposition for knowledge base completion. In International Conference on Machine Learning, 2869–2878. 2018.
[LJ18]Lisha Li and Kevin Jamieson. Hyperband: a novel bandit-based approach to hyperparameter optimization. Journal of Machine Learning Research, 18:1–52, 2018.
[MBS13]Farzaneh Mahdisoltani, Joanna Biega, and Fabian M Suchanek. Yago3: a knowledge base from multilingual wikipedias. In CIDR. 2013.
[NMTG16]Maximilian Nickel, Kevin Murphy, Volker Tresp, and Evgeniy Gabrilovich. A review of relational machine learning for knowledge graphs. Procs of the IEEE, 104(1):11–33, 2016.
[NRP+16]Maximilian Nickel, Lorenzo Rosasco, Tomaso A Poggio, and others. Holographic embeddings of knowledge graphs. In AAAI, 1955–1961. 2016.
[Pri10]Princeton. About wordnet. Web, 2010.
[SKW07]Fabian M Suchanek, Gjergji Kasneci, and Gerhard Weikum. Yago: a core of semantic knowledge. In Procs of WWW, 697–706. ACM, 2007.
[SDNT19]Zhiqing Sun, Zhi-Hong Deng, Jian-Yun Nie, and Jian Tang. Rotate: knowledge graph embedding by relational rotation in complex space. In International Conference on Learning Representations. 2019. URL:
[TCP+15]Kristina Toutanova, Danqi Chen, Patrick Pantel, Hoifung Poon, Pallavi Choudhury, and Michael Gamon. Representing text for joint embedding of text and knowledge bases. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, 1499–1509. 2015.
[TWR+16]Théo Trouillon, Johannes Welbl, Sebastian Riedel, Éric Gaussier, and Guillaume Bouchard. Complex embeddings for simple link prediction. In International Conference on Machine Learning, 2071–2080. 2016.
[YYH+14]Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng. Embedding entities and relations for learning and inference in knowledge bases. arXiv preprint, 2014.