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Knowledge Graph Foundation Models (KGFMs) are at the frontier for deep learning on knowledge graphs (KGs), as they can generalize to completely novel knowledge graphs with different relational vocabularies.
Translating embeddings for modeling multi-relational data
Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., and Yakhnenko, O · 2013
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AMIE: Association rule mining under incomplete evidence in ontological knowledge bases
Galárraga, L. A., Teflioudi, C., Hose, K., and Suchanek, F · 2013
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Yago3: A knowledge base from multilingual wikipedias
Mahdisoltani, F., Biega, J. A., and Suchanek, F. M · 2015
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Observed versus latent features for knowledge base and text inference
Toutanova, K. and Chen, D · 2015
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Layer normalization
Ba, J. L., Kiros, J. R., and Hinton, G. E · 2016
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On the representation and embedding of knowledge bases beyond binary relations
Wen, J., Li, J., Mao, Y., Chen, S., and Zhang, R · 2016
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Systematic integration of biomedical knowledge prioritizes drugs for repurposing
Himmelstein, D. S., Lizee, A., Hessler, C., Brueggeman, L., Chen, S. L., Hadley, D., Green, A., Khankhanian, P., and Baranzini, S. E · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L. u., and Polosukhin, I · 2017
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Deeppath: A reinforcement learning method for knowledge graph reasoning
Xiong, W., Hoang, T., and Wang, W. Y · 2017
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Convolutional 2D knowledge graph embeddings
Dettmers, T., Pasquale, M., Pontus, S., and Riedel, S · 2018
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Modeling relational data with graph convolutional networks
Schlichtkrull, M. S., Kipf, T. N., Bloem, P., van den Berg, R., Titov, I., and Welling, M · 2018
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Fast graph representation learning with PyTorch Geometric
Fey, M. and Lenssen, J. E · 2019
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Rotate: Knowledge graph embedding by relational rotation in complex space
Sun, Z., Deng, Z.-H., Nie, J.-Y., and Tang, J · 2019
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How powerful are graph neural networks?
Xu, K., Hu, W., Leskovec, J., and Jegelka, S · 2019
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Boxe: A box embedding model for knowledge base completion
Abboud, R., Ceylan, İ. İ., Lukasiewicz, T., and Salvatori, T · 2020
Cited alongside, same era.
Knowledge hypergraphs: Prediction beyond binary relations
Fatemi, B., Taslakian, P., Vazquez, D., and Poole, D · 2020
Cited alongside, same era.
Generalizing tensor decomposition for n-ary relational knowledge bases
Liu, Y., Yao, Q., and Li, Y · 2020
Cited alongside, same era.
Dynamic anticipation and completion for multi-hop reasoning over sparse knowledge graph
Lv, X., Xu Han, L. H., Li, J., Liu, Z., Zhang, W., Zhang, Y., Kong, H., and Wu, S · 2020
Cited alongside, same era.
CoDEx: A Comprehensive Knowledge Graph Completion Benchmark
Safavi, T. and Koutra, D · 2020
Cited alongside, same era.
Inductive relation prediction by subgraph reasoning
Teru, K. K., Denis, E. G., and Hamilton, W. L · 2020
Nodepiece: Compositional and parameter-efficient representations of large knowledge graphs
Galkin, M., Denis, E., Wu, J., and Hamilton, W. L · 2022
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Knowledge graph reasoning with relational digraph
Zhang, Y. and Yao, Q · 2022
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Double equivariance for inductive link prediction for both new nodes and new relation types
Gao, J., Zhou, Y., Zhou, J., and Ribeiro, B · 2023
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Relational message passing for fully inductive knowledge graph completion
Geng, Y., Chen, J., Pan, J. Z., Chen, M., Jiang, S., Zhang, W., and Chen, H · 2023
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A theory of link prediction via relational weisfeiler-leman on knowledge graphs
Huang, X., Orth, M. R., Ceylan, İ. İ., and Barceló, P · 2023
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Ingram: Inductive knowledge graph embedding via relation graphs
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Cited alongside, same era.
Composition-based multi-relational graph convolutional networks
Vashishth, S., Sanyal, S., Nitin, V., and Talukdar, P · 2020
Cited alongside, same era.
Neural message passing for multi-relational ordered and recursive hypergraphs
Yadati, N · 2020
Cited alongside, same era.
The logic of graph neural networks
Grohe, M · 2021
Cited alongside, same era.
Indigo: Gnn-based inductive knowledge graph completion using pair-wise encoding
Liu, S., Grau, B., Horrocks, I., and Kostylev, E · 2021
Cited alongside, same era.
Labeling trick: A theory of using graph neural networks for multi-node representation learning
Zhang, M., Li, P., Xia, Y., Wang, K., and Jin, L · 2021
Cited alongside, same era.
Neural bellman-ford networks: A general graph neural network framework for link prediction
Zhu, Z., Zhang, Z., Xhonneux, L.-P., and Tang, J · 2021
Cited alongside, same era.
Lee, J., Chung, C., and Whang, J. J · 2023
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Adaprop: Learning adaptive propagation for graph neural network based knowledge graph reasoning
Zhang, Y., Zhou, Z., Yao, Q., Chu, X., and Han, B · 2023
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A prompt-based knowledge graph foundation model for universal in-context reasoning
Cui, Y., Sun, Z., and Hu, W · 2024
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Towards foundation models for knowledge graph reasoning
Galkin, M., Yuan, X., Mostafa, H., Tang, J., and Zhu, Z · 2024
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Link prediction with relational hypergraphs
Huang, X., Orth, M. R., Barceló, P., Bronstein, M. M., and İsmail İlkan Ceylan · 2024
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Position: Graph foundation models are already here
Mao, H., Chen, Z., Tang, W., Zhao, J., Ma, Y., Zhao, T., Shah, N., Galkin, M., and Tang, J · 2024
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TRIX: A more expressive model for zero-shot domain transfer in knowledge graphs
Zhang, Y., Bevilacqua, B., Galkin, M., and Ribeiro, B · 2024
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A*net: A scalable path-based reasoning approach for knowledge graphs
Zhu, Z., Yuan, X., Galkin, M., Xhonneux, S., Zhang, M., Gazeau, M., and Tang, J · 2024
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