Bibliography

[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: https://www.microsoft.com/en-us/research/publication/observed-versus-latent-features-for-knowledge-base-and-text-inference/.

[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.

[BEP+08]

Kurt Bollacker, Colin Evans, Praveen Paritosh, Tim Sturge, and Jamie Taylor. Freebase: a collaboratively created graph database for structuring human knowledge. In Proceedings of the 2008 ACM SIGMOD international conference on Management of data, 1247–1250. AcM, 2008.

[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.

[CCS+19]

Xuelu Chen, Muhao Chen, Weijia Shi, Yizhou Sun, and Carlo Zaniolo. Embedding uncertain knowledge graphs. In AAAI, volume 33, 3363–3370. 2019.

[DMSR18]

Tim Dettmers, Pasquale Minervini, Pontus Stenetorp, and Sebastian Riedel. Convolutional 2d knowledge graph embeddings. In Procs of AAAI. 2018. URL: https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/17366.

[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: http://arxiv.org/abs/1702.05563, arXiv:1702.05563.

[KBK17]

Rudolf Kadlec, Ondrej Bajgar, and Jan Kleindienst. Knowledge base completion: baselines strike back. CoRR, 2017. URL: http://arxiv.org/abs/1705.10744, 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.

[Mil95]

George A Miller. Wordnet: a lexical database for english. Communications of the ACM, 38(11):39–41, 1995.

[MCH+18]

Tom Mitchell, William Cohen, Hruschka, and others. Never-ending learning. Communications of the ACM, 61(5):103–115, 2018.

[NNNP18]

Dai Quoc Nguyen, Tu Dinh Nguyen, Dat Quoc Nguyen, and Dinh Phung. A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network. In Proceedings of the 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), 327–333. 2018.

[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.

[PC21]

Sumit Pai and Luca Costabello. Learning Embeddings from Knowledge Graphs With Numeric Edge Attributes. In IJCAI. 2021. URL: https://arxiv.org/abs/2105.08683.

[P+99]

John Platt and others. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Advances in large margin classifiers, 10(3):61–74, 1999.

[Pri10]

Princeton. About wordnet. Web, 2010. https://wordnet.princeton.edu.

[SCMN13]

Richard Socher, Danqi Chen, Christopher D Manning, and Andrew Ng. Reasoning with neural tensor networks for knowledge base completion. In Advances in neural information processing systems, 926–934. 2013.

[SCH16]

Robyn Speer, Joshua Chin, and Catherine Havasi. Conceptnet 5.5: an open multilingual graph of general knowledge. CoRR, 2016. URL: http://arxiv.org/abs/1612.03975, arXiv:1612.03975.

[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: https://openreview.net/forum?id=HkgEQnRqYQ.

[SM+16]

Damian Szklarczyk, John H Morris, and others. The string database in 2017: quality-controlled protein–protein association networks, made broadly accessible. Nucleic acids research, pages gkw937, 2016.

[TC20]

Pedro Tabacof and Luca Costabello. Probability Calibration for Knowledge Graph Embedding Models. In ICLR. 2020. URL: https://arxiv.org/abs/1912.10000.

[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.