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Knowledge graphs (KGs) have become valuable knowledge resources in various applications, and knowledge graph embedding (KGE) methods have garnered increasing attention in recent years.
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J. Z. Pan · 2009
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AMIE: association rule mining under incomplete evidence in ontological knowledge bases
L. A. Galárraga, C. Teflioudi, K. Hose, and F. M. Suchanek · 2013
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R. Xie, Z. Liu, J. Jia, H. Luan, and M. Sun · 2016
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Knowledge transfer for out-of-knowledge-base entities : A graph neural network approach
T. Hamaguchi, H. Oiwa, M. Shimbo, and Y. Matsumoto · 2017
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Exploiting Linked Data and Knowledge Graphs for Large Organisations
J. Z. Pan, G. Vetere, J.M. Gomez-Perez, and H. Wu, editors · 2017
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Knowledge graph embedding: A survey of approaches and applications
Q. Wang, Z. Mao, B. Wang, and L. Guo · 2017
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F. Yang, Z. Yang, and W. W. Cohen · 2017
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Fine-grained evaluation of rule- and embedding-based systems for knowledge graph completion
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Open-world knowledge graph completion
B. Shi and T. Weninger · 2018
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One-shot relational learning for knowledge graphs
W. Xiong, M. Yu, S. Chang, X. Guo, and W. Y. Wang · 2018
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Meta relational learning for few-shot link prediction in knowledge graphs
M. Chen, W. Zhang, W. Zhang, Q. Chen, and H. Chen · 2019
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Anytime bottom-up rule learning for knowledge graph completion
C. Meilicke, M. W. Chekol, D. Ruffinelli, et al · 2019
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DRUM: end-to-end differentiable rule mining on knowledge graphs
A. Sadeghian, M. Armandpour, P. Ding, and D. Wang · 2019
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An open-world extension to knowledge graph completion models
H. Shah, J. Villmow, A. Ulges, U. Schwanecke, and F. Shafait · 2019
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Logic attention based neighborhood aggregation for inductive knowledge graph embedding
P. Wang, J. Han, C. Li, and R. Pan · 2019
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Out-of-sample representation learning for knowledge graphs
M. Albooyeh, R. Goel, and S. M. Kazemi · 2020
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Learning to extrapolate knowledge: Transductive few-shot out-of-graph link prediction
J. Baek, D. Bok Lee, and S. Ju Hwang · 2020
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Explainable link prediction for emerging entities in knowledge graphs
R. Bhowmik and G. de Melo · 2020
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VN network: Embedding newly emerging entities with virtual neighbors
Y. He, Z. Wang, P. Zhang, Z. Tu, and Z. Ren · 2020
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Generative adversarial zero-shot relational learning for knowledge graphs
P. Qin, X. Wang, W. Chen, C. Zhang, W. Xu, and W. Y. Wang · 2020
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Adaptive attentional network for few-shot knowledge graph completion
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Inductive relation prediction by subgraph reasoning
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Schema Aware Iterative Knowledge Graph Completion
K. Wiharja, J. Z. Pan, M. J. Kollingbaum, and Y. Deng · 2020
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Few-shot knowledge graph completion
C. Zhang, H. Yao, C. Huang, M. Jiang, Z. Li, and N. V. Chawla · 2020
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M. Ali, M. Berrendorf, M. Galkin, V. Thost, T. Ma, V. Tresp, and J. Lehmann · 2021
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Geometric models for (temporally) attributed description logics
C. Bourgaux, A. Ozaki, and J. Z. Pan · 2021
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Topology-aware correlations between relations for inductive link prediction in knowledge graphs
J. Chen, H. He, F. Wu, and J. Wang · 2021
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Inductive entity representations from text via link prediction
D. Daza, M. Cochez, and P. Groth · 2021
Nodepiece: Compositional and parameter-efficient representations of large knowledge graphs
M. Galkin, E. G. Denis, J. Wu, and W. L. Hamilton · 2022
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Inductive logical query answering in knowledge graphs
M. Galkin, Z. Zhu, H. Ren, and J. Tang · 2022
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Disentangled ontology embedding for zero-shot learning
Y. Geng, J. Chen, W. Zhang, Y. Xu, Z. Chen, J. Z. Pan, Y. Huang, F. Xiong, and H. Chen · 2022
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Few-shot relational reasoning via connection subgraph pretraining
Q. Huang, H. Ren, and J. Leskovec · 2022
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Inductive relation prediction using analogy subgraph embeddings
J. Jin, Y. Wang, K. Du, W. Zhang, Z. Zhang, D. Wipf, Y. Yu, and Q. Gan · 2022
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HAPZSL: A hybrid attention prototype network for knowledge graph zero-shot relational learning
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Ontozsl: Ontology-enhanced zero-shot learning
Y. Geng, J. Chen, Z. Chen, J. Z. Pan, Z. Ye, Z. Yuan, Y. Jia, and H. Chen · 2021
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Metap: Meta pattern learning for one-shot knowledge graph completion
Z. Jiang, J. Gao, and X. Lv · 2021
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INDIGO: gnn-based inductive knowledge graph completion using pair-wise encoding
S. Liu, B. C. Grau, I. Horrocks, and E. V. Kostylev · 2021
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Communicative message passing for inductive relation reasoning
S. Mai, S. Zheng, Y. Yang, and H. Hu · 2021
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Relational learning with gated and attentive neighbor aggregator for few-shot knowledge graph completion
G. Niu, Y. Li, C. Tang, R. Geng, J. Dai, Q. Liu, H. Wang, J. Sun, F. Huang, and L. Si · 2021
Cited alongside, same era.
Structure-augmented text representation learning for efficient knowledge graph completion
B. Wang, T. Shen, G. Long, T. Zhou, Y. Wang, and Y. Chang · 2021
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X. Li, J. Ma, J. Yu, T. Xu, M. Zhao, H. Liu, M. Yu, and R. Yu · 2022
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Reasoning over different types of knowledge graphs: Static, temporal and multi-modal
K. Liang, L. Meng, M. Liu, Y. Liu, W. Tu, S. Wang, S. Zhou, X. Liu, and F. Sun · 2022
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Incorporating context graph with logical reasoning for inductive relation prediction
Q. Lin, J. Liu, F. Xu, et al · 2022
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Statik: Structure and text for inductive knowledge graph completion
E. Markowitz, K. Balasubramanian, M. Mirtaheri, M. Annavaram, A. Galstyan, and G. Ver Steeg · 2022
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Decoupling mixture-of-graphs: Unseen relational learning for knowledge graph completion by fusing ontology and textual experts
R. Song, S. He, S. Zheng, S. Gao, K. Liu, Z. Yu, and J. Zhao · 2022
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Exploring relational semantics for inductive knowledge graph completion
C. Wang, X. Zhou, S. Pan, L. Dong, Z. Song, and Y. Sha · 2022
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Simkgc: Simple contrastive knowledge graph completion with pre-trained language models
L. Wang, W. Zhao, Z. Wei, and J. Liu · 2022
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Facing changes: Continual entity alignment for growing knowledge graphs
Y. Wang, Y. Cui, W. Liu, et al · 2022
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Subgraph neighboring relations infomax for inductive link prediction on knowledge graphs
X. Xu, P. Zhang, Y. He, et al · 2022
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Cycle representation learning for inductive relation prediction
Z. Yan, T. Ma, L. Gao, Z. Tang, and C. Chen · 2022
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Inductive relation prediction by BERT
H. Zha, Z. Chen, and X. Yan · 2022
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Knowledge graph reasoning with relational digraph
Y. Zhang and Q. Yao · 2022
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Lifelong embedding learning and transfer for growing knowledge graphs
Y. Cui, Y. Wang, Z. Sun, W. Liu, Y. Jiang, K. Han, and W. Hu · 2023
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Relational message passing for fully inductive knowledge graph completion
Y. Geng, J. Chen, W. Zhang, J. Z. Pan, M. Chen, H. Chen, and S. Jiang · 2023
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An empirical study of pre-trained language models in simple knowledge graph question answering
N. Hu, Y. Wu, G. Qi, D. Min, J. Chen, J. Z Pan, and Z. Ali · 2023
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Neuralkg-ind: A python library for inductive knowledge graph representation learning
W. Zhang, Z. Yao, M. Chen, Z. Huang, and H. Chen · 2023
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