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An emerging trend in representation learning over knowledge graphs (KGs) moves beyond transductive link prediction tasks over a fixed set of known entities in favor of inductive tasks that imply training on one graph and performing inference over a new graph with unseen entities.
On the Ambiguity of Rank-Based Evaluation of Entity Alignment or Link Prediction Methods
Max Berrendorf, Evgeniy Faerman, Laurent Vermue, and Volker Tresp. 2020 · 2002
Earlier work this paper cites.
CoDEx: A Comprehensive Knowledge Graph Completion Benchmark
Tara Safavi and Danai Koutra. 2020 · 2009
Earlier work this paper cites.
A Three-Way Model for Collective Learning on Multi-Relational Data. In Proceedings of the 28th International Conference on Machine Learning, ICML 2011, Bellevue, Washington, USA, June 28 - July 2, 2011 , Lise Getoor and Tobias Scheffer (Eds.). Omnipress, 809–816
Maximilian Nickel, Volker Tresp, and Hans-Peter Kriegel. 2011 · 2011
Earlier work this paper cites.
Wikidata: a free collaborative knowledgebase
Denny Vrandečić and Markus Krötzsch. 2014 · 2014
Earlier work this paper cites.
Adam: A Method for Stochastic Optimization. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings , Yoshua Bengio and Yann LeCun (Eds.)
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
Earlier work this paper cites.
Observed versus latent features for knowledge base and text inference. In Proceedings of the 3rd Workshop on Continuous Vector Space Models and their Compositionality . Association for Computational Linguistics, Beijing, China, 57–66
Kristina Toutanova and Danqi Chen. 2015 · 2015
Earlier work this paper cites.
Embedding Entities and Relations for Learning and Inference in Knowledge Bases. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings , Yoshua Bengio and Yann LeCun (Eds.)
Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng. 2015 · 2015
Earlier work this paper cites.
Variational Knowledge Graph Reasoning. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) . 1823–1832
Wenhu Chen, Wenhan Xiong, Xifeng Yan, and William Yang Wang. 2018 · 2018
Earlier work this paper cites.
Convolutional 2D Knowledge Graph Embeddings. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), New Orleans, Louisiana, USA, February 2-7, 2018 , Sheila A. McIlraith and Kilian Q. Weinberger (Eds.). AAAI Press, 1811–1818
Tim Dettmers, Pasquale Minervini, Pontus Stenetorp, and Sebastian Riedel. 2018 · 2018
Cited alongside, same era.
Look Before You Hop: Conversational Question Answering over Knowledge Graphs Using Judicious Context Expansion. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (Beijing, China) (CIKM ’19) . 729–738
Philipp Christmann, Rishiraj Saha Roy, Abdalghani Abujabal, Jyotsna Singh, and Gerhard Weikum. 2019 · 2019
Cited alongside, same era.
RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space. In 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019 . OpenReview.net
Zhiqing Sun, Zhi-Hong Deng, Jian-Yun Nie, and Jian Tang. 2019 · 2019
Cited alongside, same era.
PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings
Mehdi Ali, Max Berrendorf, Charles Tapley Hoyt, Laurent Vermue, Sahand Sharifzadeh, Volker Tresp, and Jens Lehmann. 2021c · 2021
Later among the works it cites.
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
Michael M. Bronstein, Joan Bruna, Taco Cohen, and Petar Veličković. 2021 · 2021
Later among the works it cites.
Inductive Entity Representations from Text via Link Prediction. In Proceedings of The Web Conference 2021
Daniel Daza, Michael Cochez, and Paul Groth. 2021 · 2021
Later among the works it cites.
OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs
Weihua Hu, Matthias Fey, Hongyu Ren, Maho Nakata, Yuxiao Dong, and Jure Leskovec. 2021 · 2021
Later among the works it cites.
KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation
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Open graph benchmark: Datasets for machine learning on graphs
Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec. 2020 · 2020
Cited alongside, same era.
You CAN Teach an Old Dog New Tricks! On Training Knowledge Graph Embeddings. In International Conference on Learning Representations
Daniel Ruffinelli, Samuel Broscheit, and Rainer Gemulla. 2020 · 2020
Cited alongside, same era.
Inductive relation prediction by subgraph reasoning. In International Conference on Machine Learning . PMLR, 9448–9457
Komal Teru, Etienne Denis, and Will Hamilton. 2020 · 2020
Cited alongside, same era.
Composition-based Multi-Relational Graph Convolutional Networks. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020 . OpenReview.net
Shikhar Vashishth, Soumya Sanyal, Vikram Nitin, and Partha P. Talukdar. 2020 · 2020
Cited alongside, same era.
Bringing Light Into the Dark: A Large-scale Evaluation of Knowledge Graph Embedding Models under a Unified Framework
Mehdi Ali, Max Berrendorf, Charles Tapley Hoyt, Laurent Vermue, Mikhail Galkin, Sahand Sharifzadeh, Asja Fischer, Volker Tresp, and Jens Lehmann. 2021b · 2021
Cited alongside, same era.
Improving Inductive Link Prediction Using Hyper-relational Facts. In International Semantic Web Conference . Springer, 74–92
Mehdi Ali, Max Berrendorf, Mikhail Galkin, Veronika Thost, Tengfei Ma, Volker Tresp, and Jens Lehmann. 2021a
Cited in the paper.
Autoregressive Entity Retrieval. In International Conference on Learning Representations
Nicola De Cao, Gautier Izacard, Sebastian Riedel, and Fabio Petroni. 2021a
Cited in the paper.
Multilingual Autoregressive Entity Linking. In arXiv pre-print 2103.12528
Nicola De Cao, Ledell Wu, Kashyap Popat, Mikel Artetxe, Naman Goyal, Mikhail Plekhanov, Luke Zettlemoyer, Nicola Cancedda, Sebastian Riedel, and Fabio Petroni. 2021b
Cited in the paper.
Xiaozhi Wang, Tianyu Gao, Zhaocheng Zhu, Zhengyan Zhang, Zhiyuan Liu, Juanzi Li, and Jian Tang. 2021 · 2021
Later among the works it cites.
Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction. In Advances in Neural Information Processing Systems , A. Beygelzimer, Y. Dauphin, P. Liang, and J. Wortman Vaughan (Eds.)
Zhaocheng Zhu, Zuobai Zhang, Louis-Pascal A. C. Xhonneux, and Jian Tang. 2021 · 2021
Later among the works it cites.
NodePiece: Compositional and Parameter-Efficient Representations of Large Knowledge Graphs. In International Conference on Learning Representations
Mikhail Galkin, Etienne Denis, Jiapeng Wu, and William L. Hamilton. 2022 · 2022
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migalkin/ilpc2022: Initial pre-release
Michael Galkin and Charles Tapley Hoyt. 2022 · 2022
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