Hypernetwork Knowledge Graph Embeddings
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The third group of KGE models are neural-network-based models, such as ConvE, ConvKB, HypER, CompGCN, SACN and CNN-BiLSTM.
“The third group are neural-networkbased models, such as ConvE [22], ConvKB [23], HypER [24], CompGCN [25], SACN [26] and CNN-BiLSTM [27].”
From A Survey on Knowledge Graph Embeddings for Link Prediction (2021) · cites this paper as [24]
HypER implements nonlinear quadratic combinations of entity and relation embeddings rather than the linear combinations in ConvE, giving HypER richer expressive capacity and fewer parameters.
“HypER implements nonlinear (quadratic) combinations of entity and relation embeddings rather than the linear combinations (weighted sums) in ConvE, which gives HypER the advantages of a much richer expressive capacity and fewer parameters.”
From A Survey on Knowledge Graph Embeddings for Link Prediction (2021) · cites this paper as [24]
RotatE and HypER outperform the other implemented models in the experiments.
“As shown in Table 5, RotatE [35] and HypER [24] outperform the other models”
From A Survey on Knowledge Graph Embeddings for Link Prediction (2021) · cites this paper as [24]
HypER can be understood in terms of tensor factorization up to a non-linearity.
“The authors show that convolution is a way of introducing sparsity and parameter tying and that HypER can be understood in terms of tensor factorization up to a non-linearity, thus placing HypER closer to the well established family of factorization models.”
From TuckER: Tensor Factorization for Knowledge Graph Completion (2019) · cites this paper as Balažević et al., 2019
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Cites
Complex Embeddings for Simple Link Prediction
Trouillon, T., Welbl, J., Riedel, S., Gaussier, É., & Bouchard, G. (2016) · arXiv · doi:10.48550/arxiv.1606.06357 · Cited by 2,100 · see citing works
RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space
Sun, Z., Deng, Z.-H., Nie, J.-Y., & Tang, J. (2019) · arXiv · doi:10.48550/arxiv.1902.10197 · Cited by 1,414 · see citing works
SimplE Embedding for Link Prediction in Knowledge Graphs
Kazemi, S. M., & Poole, D. (2018) · arXiv · doi:10.48550/arxiv.1802.04868 · Cited by 476 · see citing works
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Research works printed but not linked: 16
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Cited by
Sorted by citations
A Survey on Knowledge Graphs: Representation, Acquisition, and Applications
Ji, S., Pan, S., Cambria, E., Marttinen, P., & Yu, P. S. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3070843 · Cited by 2,947 · see citing works
TuckER: Tensor Factorization for Knowledge Graph Completion
Balazevic, I., Allen, C., & Hospedales, T. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1522 · Cited by 696 · see citing works
Self-citationA Survey on Knowledge Graph Embeddings for Link Prediction
Wang, M., Qiu, L., & Wang, X. (2021) · Symmetry · doi:10.3390/sym13030485 · Cited by 212 · see citing works
- Authors
- Balažević, I., Allen, C., & Hospedales, T. M.
- Affiliations
- University of Edinburgh (GB)
- Corresponding author
- Balažević, Ivana
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2019
- Pages
- 553-565
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2888572441
- Cited by
- 229
- References
- 3
- Citations by year
- 2026: 8 · 2025: 28 · 2024: 44 · 2023: 49 · 2022: 38 · 2021: 41 · 2020: 15 · 2019: 6
- FWCI
- 9.56
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, other-oa)
- arXiv (Cornell University)
- Funders
- Engineering and Physical Sciences Research Council (EP/L016427/1)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Keywords
- knowledge graph embedding, link prediction, hypernetworks, tensor decomposition, parameter tying, sparsity, knowledge graph incompleteness
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