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The dominant paradigm for relation prediction in knowledge graphs involves learning and operating on latent representations (i.e., embeddings) of entities and relations.
Approximation capabilities of multilayer feedforward networks
Hornik, K · 1991
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The graph neural network model
Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., and Monfardini, G · 2008
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Translating embeddings for modeling multi-relational data
Bordes, A., Usunier, N., García-Durán, 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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Embedding entities and relations for learning and inference in knowledge bases
Yang, B., tau Yih, W., He, X., Gao, J., and Deng, L · 2014
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Representing text for joint embedding of text and knowledge bases
Toutanova, K., Chen, D., Pantel, P., Poon, H., Choudhury, P., and Gamon, M · 2015
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Tensorlog: A differentiable deductive database
Cohen, W. W · 2016
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Gated graph sequence neural networks
Li, Y., Tarlow, D., Brockschmidt, M., and Zemel, R · 2016
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A review of relational machine learning for knowledge graphs
Nickel, M., Murphy, K., Tresp, V., and Gabrilovich, E · 2016
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Revisiting semi-supervised learning with graph embed dings
Yang, Z., Cohen, W. W., and Salakhutdinov, R · 2016
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Geometric deep learning: going beyond euclidean data
Bronstein, M. M., Bruna, J., LeCun, Y., Szlam, A., and Vandergheynst, P · 2017
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Neural message passing for quantum chemistry
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., and Dahl, G. E · 2017
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Knowledge transfer for out-of-knowledge-base entities: A graph neural network approach
Hamaguchi, T., Oiwa, H., Shimbo, M., and Matsumoto, Y · 2017
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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 · 2017
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Knowledge graph completion via complex tensor factorization
Trouillon, T., Dance, C. R., Éric Gaussier, Welbl, J., Riedel, S., and Bouchard, G · 2017
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Deeppath: A reinforcement learning method for knowledge graph reasoning
Fine-grained evaluation of rule- and embedding-based systems for knowledge graph completion
Meilicke, C., Fink, M., Wang, Y., Ruffinelli, D., Gemulla, R., and Stuckenschmidt, H · 2018
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Representation learning on graphs with jumping knowledge networks
Xu, K., Li, C., Tian, Y., Sonobe, T., ichi Kawarabayashi, K., and Jegelka, S · 2018
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Link prediction based on graph neural networks
Zhang, M. and Chen, Y · 2018
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Drum: End-to-end differentiable rule mining on knowledge graphs
Sadeghian, A., Armandpour, M., Ding, P., and Wang, D. Z · 2019
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CLUTRR: A diagnostic benchmark for inductive reasoning from text
Sinha, K., Sodhani, S., Dong, J., Pineau, J., and Hamilton, W. L · 2019
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Rotate: Knowledge graph embedding by relational rotation in complex space
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Xiong, W., Hoang, T., and Wang, W. Y · 2017
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Differentiable learning of logical rules for knowledge base reasoning
Yang, F., Yang, Z., and Cohen, W. W · 2017
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Deep gaussian embedding of attributed graphs: Unsupervised inductive learning via ranking
Bojchevski, A. and Günnemann, S · 2018
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Convolutional 2d knowledge graph embeddings
Dettmers, T., Pasquale, M., Pontus, S., and Riedel, S · 2018
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Representation learning on graphs: Methods and applications
Hamilton, W. L., Ying, R., and Leskovec, J
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Inductive representation learning on large graphs
Hamilton, W. L., Ying, R., and Leskovec, J
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Sun, Z., Deng, Z.-H., Nie, J.-Y., and Tang, J · 2019
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Logic attention based neighborhood aggregation for inductive knowledge graph embedding
Wang, P., Han, J., Li, C., and Pan, R · 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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Inductive graph pattern learning for recommender systems based on a graph neural network
Zhang, M. and Chen, Y · 2020
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