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Sparse knowledge graph (KG) scenarios pose a challenge for previous Knowledge Graph Completion (KGC) methods, that is, the completion performance decreases rapidly with the increase of graph sparsity.
Pattern recognition and machine learning , volume 4
Bishop, C. M. and Nasrabadi, N. M · 2006
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Markov logic networks
Richardson, M. and Domingos, P · 2006
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Translating embeddings for modeling multi-relational data
Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., and Yakhnenko, O · 2013
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Embedding entities and relations for learning and inference in knowledge bases
Yang, B., Yih, S. W.-t., He, X., Gao, J., and Deng, L · 2015
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Semi-supervised classification with graph convolutional networks
Kipf, T. and Welling, M · 2016
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Complex embeddings for simple link prediction
Trouillon, T., Welbl, J., Riedel, S., Gaussier, É., and Bouchard, G · 2016
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Convolutional 2d knowledge graph embeddings
Dettmers, T., Minervini, P., Stenetorp, P., and Riedel, S · 2018
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Multi-hop knowledge graph reasoning with reward shaping
Lin, X. V., Socher, R., and Xiong, C · 2018
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Modeling relational data with graph convolutional networks
Schlichtkrull, M., Kipf, T. N., Bloem, P., Van Den Berg, R., Titov, I., and Welling, M · 2018
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Rotate: Knowledge graph embedding by relational rotation in complex space
Sun, Z., Deng, Z., Nie, J.-Y., and Tang, J · 2018
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Node, motif and subgraph: Leveraging network functional blocks through structural convolution
Yang, C., Liu, M., Zheng, V. W., and Han, J · 2018
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Tucker: Tensor factorization for knowledge graph completion
Balažević, I., Allen, C., and Hospedales, T · 2019
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Probabilistic logic neural networks for reasoning
Qu, M. and Tang, J · 2019
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End-to-end structure-aware convolutional networks for knowledge base completion
Shang, C., Tang, Y., Huang, J., Bi, J., He, X., and Zhou, B · 2019
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Composition-based multi-relational graph convolutional networks
Vashishth, S., Sanyal, S., Nitin, V., and Talukdar, P. P · 2019
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Dynamic anticipation and completion for multi-hop reasoning over sparse knowledge graph
Lv, X., Han, X., Hou, L., Li, J., Liu, Z., Zhang, W., Zhang, Y., Kong, H., and Wu, S · 2020
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A re-evaluation of knowledge graph completion methods
Kqa pro: A dataset with explicit compositional programs for complex question answering over knowledge base
Cao, S., Shi, J., Pan, L., Nie, L., Xiang, Y., Hou, L., Li, J., He, B., and Zhang, H · 2022
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Explainable sparse knowledge graph completion via high-order graph reasoning network
Chen, W., Cao, Y., Feng, F., He, X., and Zhang, Y · 2022
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Lfkqg: A controlled generation framework with local fine-tuning for question generation over knowledge bases
Fei, Z., Zhou, X., Gui, T., Zhang, Q., and Huang, X.-J · 2022
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Inductive logical query answering in knowledge graphs
Galkin, M., Zhu, Z., Ren, H., and Tang, J · 2022
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Graph neural network for higher-order dependency networks
Jin, D., Gong, Y., Wang, Z., Yu, Z., He, D., Huang, Y., and Wang, W · 2022
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Sun, Z., Vashishth, S., Sanyal, S., Talukdar, P., and Yang, Y · 2020
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Efficient probabilistic logic reasoning with graph neural networks
Zhang, Y., Chen, X., Yang, Y., Ramamurthy, A., Li, B., Qi, Y., and Song, L · 2020
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Knowledge graph embedding for link prediction: A comparative analysis
Rossi, A., Barbosa, D., Firmani, D., Matinata, A., and Merialdo, P · 2021
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Understanding over-squashing and bottlenecks on graphs via curvature
Topping, J., Giovanni, F. D., Chamberlain, B. P., Dong, X., and Bronstein, M. M · 2021
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Reasoning like human: Hierarchical reinforcement learning for knowledge graph reasoning
Wan, G., Pan, S., Gong, C., Zhou, C., and Haffari, G · 2021
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Neural bellman-ford networks: A general graph neural network framework for link prediction
Zhu, Z., Zhang, Z., Xhonneux, L.-P., and Tang, J · 2021
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How does knowledge graph embedding extrapolate to unseen data: a semantic evidence view
Li, R., Cao, Y., Zhu, Q., Bi, G., Fang, F., Liu, Y., and Li, Q
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C3kg: A chinese commonsense conversation knowledge graph
Li, D., Li, Y., Zhang, J., Li, K., Wei, C., Cui, J., and Wang, B · 2022
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Knowledge graph reasoning with relational digraph
Zhang, Y. and Yao, Q · 2022
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Joint entity and relation extraction with set prediction networks
Sui, D., Zeng, X., Chen, Y., Liu, K., and Zhao, J · 2023
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Kracl: contrastive learning with graph context modeling for sparse knowledge graph completion
Tan, Z., Chen, Z., Feng, S., Zhang, Q., Zheng, Q., Li, J., and Luo, M · 2023
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How to unleash the power of large language models for few-shot relation extraction?
Xu, X., Zhu, Y., Wang, X., and Zhang, N · 2023
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