Processing Systems 35 (NeurIPS), volume 35, pages 29944-29959. Curran Associates, Inc., 2022.
[2] Erik Arakelyan, Daniel Daza, Pasquale Minervini, and Michael Cochez. Complex query answering with neural link predictors. In ICLR. OpenReview.net, 2021.
[3] Ivana Balazevic, Carl Allen, and Timothy M. Hospedales. Tucker: Tensor factorization for knowledge graph completion. In EMNLP-IJCNLP, pages 5184-5193. Association for Computational Linguistics, 2019.
[4] Mikolaj Binkowski, Danica J. Sutherland, Michael Arbel, and Arthur Gretton. Demystifying MMD gans. In ICLR (Poster). OpenReview.net, 2018.
[5] Stephen Bonner, Ian P Barrett, Cheng Ye, Rowan Swiers, Ola Engkvist, Charles Tapley Hoyt, and William L Hamilton. Understanding the performance of knowledge graph embeddings in drug discovery. Artificial Intelligence in the Life Sciences, 2:100036, 2022.
[6] Antoine Bordes, Jason Weston, Ronan Collobert, and Yoshua Bengio. Learning structured embeddings of knowledge bases. Proceedings of the AAAI Conference on Artificial Intelligence, 2011.
[7] Antoine Bordes, Nicolas Usunier, Alberto García-Durán, Jason Weston, and Oksana Yakhnenko. Translating embeddings for modeling multi-relational data. In Neural Information Processing Systems (NIPS), pages 2787-2795, 2013.
[8] Kai-Wei Chang, Wen-tau Yih, Bishan Yang, and Christopher Meek.Typed tensor decomposition of knowledge bases for relation extraction. In EMNLP, pages 1568-1579. ACL, 2014.
[9] Jatin Chauhan, Priyanshu Gupta, and Pasquale Minervini. A probabilistic framework for knowledge graph data augmentation. arXiv preprint arXiv:2110.13205, 2021.
[10] Mark Chavira and Adnan Darwiche. On probabilistic inference by weighted model counting. Artificial Intelligence., 172(6-7):772-799, 2008.
[11] Xuelu Chen, Michael Boratko, Muhao Chen, Shib Sankar Dasgupta, Xiang Lorraine Li, and Andrew McCallum. Probabilistic box embeddings for uncertain knowledge graph reasoning. arXiv preprint arXiv:2104.04597, 2021.
[12] Yihong Chen, Pasquale Minervini, Sebastian Riedel, and Pontus Stenetorp. Relation prediction as an auxiliary training objective for improving multi-relational graph representations. CoRR, abs/2110.02834, 2021.
[13] YooJung Choi, Antonio Vergari, and Guy Van den Broeck. Probabilistic circuits: A unifying framework for tractable probabilistic modeling. 2020.
[14] Nilesh Dalvi and Dan Suciu. Efficient query evaluation on probabilistic databases. The VLDB Journal, 16(4):523-544, 2007.
[15] Nilesh N. Dalvi and Dan Suciu. The dichotomy of probabilistic inference for unions of conjunctive queries. ACM, 59(6):30:1-30:87, 2012.
[16] Meihua Dang, Antonio Vergari, and Guy Broeck. Strudel: Learning structured-decomposable probabilistic circuits. In International Conference on Probabilistic Graphical Models, pages 137-148. PMLR, 2020.
[17] Adnan Darwiche. Modeling and Reasoning with Bayesian Networks. Cambridge University Press, 2009.
[18] Adnan Darwiche. SDD: A new canonical representation of propositional knowledge bases. In Twenty-Second International Joint Conference on Artificial Intelligence, 2011.
[19] Adnan Darwiche and Pierre Marquis. A knowledge compilation map. Journal of Artificial Intelligence Research (JAIR), 17:229-264, 2002.