Low-Dimensional Hyperbolic Knowledge Graph Embeddings
Open accessAbstract
Knowledge graph (KG) embeddings learn lowdimensional representations of entities and relations to predict missing facts.KGs often exhibit hierarchical and logical patterns which must be preserved in the embedding space.For hierarchical data, hyperbolic embedding methods have shown promise for high-fidelity and parsimonious representations.However, existing hyperbolic embedding methods do not account for the rich logical patterns in KGs.In this work, we introduce a class of hyperbolic KG embedding models that simultaneously capture hierarchical and logical patterns.Our approach combines hyperbolic reflections and rotations with attention to model complex relational patterns.Experimental results on standard KG benchmarks show that our method improves over previous Euclidean-and hyperbolic-based efforts by up to 6.1% in mean reciprocal rank (MRR) in low dimensions.Furthermore, we observe that different geometric transformations capture different types of relations while attentionbased transformations generalize to multiple relations.In high dimensions, our approach yields new state-of-the-art MRRs of 49.6% on WN18RR and 57.7% on YAGO3-10.
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Methods for reasoning over KGs that use hyperbolic space (MuRP, RotH) use relational transformations (rotation) that prevent them from faithfully capturing all properties of hierarchical relations.
“While there are methods for reasoning over KGs that use hyperbolic space (MuRP [15], RotH [16]), which is suitable for modeling tree-like graphs, the choice of relational transformations used in these works (rotation) prevents them from faithfully capturing all the properties of hierarchical relations.”
From Modeling Heterogeneous Hierarchies with Relation-specific Hyperbolic Cones (2021) · cites this paper as [16]
The paper does not describe MuRP as a rotation model: its Complex embeddings section calls MuRP a translation model. Section 4.2 says rotations can model several logical patterns, and Section 5.6 reports that RotH preserves tree structure. These passages contradict the claim that rotation prevents these methods from faithfully capturing hierarchical relations. (LLM, conf=0.86)
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Experimental — accuracy not yet measuredTransformations used in MuRP and RotH cannot capture transitive relations, which hierarchical relations naturally are.
“However, transformations used in MuRP and RotH cannot capture transitive relations which hierarchical relations naturally are.”
From Modeling Heterogeneous Hierarchies with Relation-specific Hyperbolic Cones (2021) · cites this paper as [16]
The discussion of MuRP says it fails to encode some logical properties, but does not identify transitivity as one of them. Section 5.6 says RotH preserves tree structure, and the paper does not say that RotH cannot capture transitive relations. The specific claim about both models therefore exceeds what the source supports. (LLM, conf=0.82)
Experimental — accuracy not yet measuredRotH uses rotation and reflection transformation in n-dimensional Poincaré space to model relational patterns, and achieves state-of-the-art for the KG completion task, especially under low-dimensionality.
“RotH [16] uses rotation and reflection transformation in n -dimensional Poincaré space to model relational patterns, and achieves state-of-the-art for the KG completion task, especially under low-dimensionality.”
From Modeling Heterogeneous Hierarchies with Relation-specific Hyperbolic Cones (2021) · cites this paper as [16]
The Ablations passage (WN18RR section; page unavailable) defines RotH as a variant using only rotations, while §4.4 assigns the combination of rotations and reflections to AttH. Figure 5(a) reports improved low-dimensional performance for RotH, but §5.2 attributes the low-dimensional improvements over prior methods to AttH. Thus the claim identifies a relevant hyperbolic KG model but incorrectly assigns reflections and the broad state-of-the-art result to RotH. (LLM, conf=0.90)
Experimental — accuracy not yet measuredThe reason RotH needs learning space curvature while ConE does not lies in the choice of embedding space: RotH uses a 2d-dimensional hyperbolic space while ConE uses product space of d hyperbolic planes.
“The reason why RotH [16] needs learning space curvature while ConE does not lie in the choice of embedding space: RotH uses a 2 d -dimensional hyperbolic space while ConE uses product space of d hyperbolic planes.”
From Modeling Heterogeneous Hierarchies with Relation-specific Hyperbolic Cones (2021) · cites this paper as [16]
Section 4.1 says the model learns relation-specific curvature, and Section 5.5 reports that fixed curvature degrades RotH's performance in high dimensions. Section 3.2 describes a single d-dimensional Poincaré ball, but the paper does not discuss ConE or establish that the difference in embedding spaces explains why one model learns curvature and the other does not. Pages are not available in our copy. (LLM, conf=0.86)
Experimental — accuracy not yet measured
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Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
WordNet: a lexical database for English
Miller, G. A. (1995) · Communications of the ACM · doi:10.1145/219717.219748 · Cited by 14,015 · see citing works
(2010) · Cited by 8,261 · see citing works
Modeling Relational Data with Graph Convolutional Networks
Schlichtkrull, M., Kipf, T. N., Bloem, P., van den Berg, R., Titov, I., & Welling, M. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-93417-4_38 · Cited by 5,570 · see citing works
Knowledge Graph Embedding by Translating on Hyperplanes
Wang, Z., Zhang, J., Feng, J., & Chen, Z. (2014) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v28i1.8870 · Cited by 3,915 · see citing works
Suchanek, F. M., Kasneci, G., & Weikum, G. (2007) · International World Wide Web Conference (WWW) · doi:10.1145/1242572.1242667 · Cited by 3,886 · see citing works
Learning Entity and Relation Embeddings for Knowledge Graph Completion
Lin, Y., Liu, Z., Sun, M., Liu, Y., & Zhu, X. (2015) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v29i1.9491 · Cited by 3,749 · see citing works
Embedding Entities and Relations for Learning and Inference in Knowledge Bases
Yang, B., Yih, W.-T., He, X., Gao, J., & Deng, L. (2014) · arXiv · doi:10.48550/arxiv.1412.6575 · Cited by 2,252 · see citing works
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
Knowledge Graph Embedding via Dynamic Mapping Matrix
Ji, G., He, S., Xu, L., Liu, K., & Zhao, J. (2015) · Annual Meeting of the Association for Computational Linguistics · doi:10.3115/v1/p15-1067 · Cited by 1,913 · see citing works
A Three-Way Model for Collective Learning on Multi-Relational Data
Nickel, M., Tresp, V., & Kriegel, H. (2011) · Cited by 1,806 · 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
Observed versus latent features for knowledge base and text inference
Toutanova, K., & Chen, D. (2015) · Proceedings of the 3rd Workshop on Continuous Vector Space Models and their Compositionality · doi:10.18653/v1/w15-4007 · Cited by 1,070 · see citing works
A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network
Nguyen, D. Q., Nguyen, T. D., Nguyen, D. Q., & Phung, D. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers) · doi:10.18653/v1/n18-2053 · Cited by 923 · see citing works
Poincaré Embeddings for Learning Hierarchical Representations
Nickel, M., & Kiela, D. (2017) · arXiv · doi:10.48550/arxiv.1705.08039 · Cited by 667 · see citing works
Learning Attention-based Embeddings for Relation Prediction in \nKnowledge Graphs
Nathani, D., Chauhan, J., Sharma, C., & Kaul, M. (2019) · Research Archive of Indian Institute of Technology Hyderabad (Indian Institute of Technology Hyderabad) · Cited by 665 · see citing works
Convolutional 2D Knowledge Graph EmbeddingsPreprint
Dettmers, T., Minervini, P., Stenetorp, P., & Riedel, S. (2017) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.833287 · Cited by 639 · see citing works
Stochastic Gradient Descent on Riemannian Manifolds
Bonnabel, S. (2013) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2013.2254619 · Cited by 540 · see citing works
Quaternion Knowledge Graph EmbeddingsPreprint
Zhang, S., Tay, Y., Yao, L., & Liu, Q. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.10281 · Cited by 372 · see citing works
Hyperbolic Graph Convolutional Neural Networks
Chami, I., Ying, R., Ré, C., & Leskovec, J. (2019) · arXiv · doi:10.48550/arxiv.1910.12933 · Cited by 344 · see citing works
Self-citationGraph theoretical dimensions of informal organizations
(1994) · L. Erlbaum Associates Inc. eBooks · doi:10.4324/9781315806648-11 · Cited by 306 · see citing works
Canonical Tensor Decomposition for Knowledge Base Completion
Lacroix, T., Usunier, N., & Obozinski, G. (2018) · arXiv · doi:10.48550/arxiv.1806.07297 · Cited by 247 · see citing works
Hyperbolic Graph Neural NetworksPreprint
Liu, Q., Nickel, M., & Kiela, D. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.12892 · Cited by 212 · see citing works
Poincaré GloVe: Hyperbolic Word EmbeddingsPreprint
Ţifrea, A., Bécigneul, G., & Ganea, O.-E. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.06546 · Cited by 196 · see citing works
A2N: Attending to Neighbors for Knowledge Graph Inference
Bansal, T., Juan, D.-C., Ravi, S., & McCallum, A. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1431 · Cited by 135 · see citing works
Self-citationLearning Mixed-Curvature Representations in Product Spaces.
Gu, A., Sala, F., Gunel, B., & Ré, C. (2018) · International Conference on Learning Representations · Cited by 94 · see citing works
Self-citationRepresentation Tradeoffs for Hyperbolic Embeddings.
De, C., Gu, A., Ré, C., & Sala, F. (2018) · PubMed · Cited by 84 · see citing works
Self-citationRelation Embedding with Dihedral Group in Knowledge Graph
Xu, C., & Li, R. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1026 · Cited by 69 · see citing works
Introduction to Differential Geometry
Lévine, J. (2009) · Springer eBooks · doi:10.1007/978-3-642-00839-9_2 · Cited by 33 · see citing works
Flagged first, then by citations
4 of the 33 references couldn’t be matched to a known paper.
Research works printed but not linked: 5
- Ivana Balažević, Carl Allen, and Timothy Hospedales. 2019. Multi-relational poincaré graph embeddings. In Advances in Neural Information Processing Sys- tems, pages 4465-4475.
- Ivana Balazevic, Carl Allen, and Timothy Hospedales. 2019. Tucker: Tensor factorization for knowledge graph completion. In Proceedings of the 2019 Con- ference on Empirical Methods in Natural Language Processing and the 9th International Joint Confer- ence on Natural Language Processing (EMNLP- IJCNLP), pages 5188-5197.
- Antoine Bordes, Nicolas Usunier, Alberto Garcia- Duran, Jason Weston, and Oksana Yakhnenko. 2013. Translating embeddings for modeling multi- relational data. In Advances in Neural Information Processing Systems, pages 2787-2795.
- Octavian Ganea, Gary Bécigneul, and Thomas Hof- mann. 2018. Hyperbolic neural networks. In Ad- vances in Neural Information Processing Systems.
- Fabian M Suchanek, Gjergji Kasneci, and Gerhard Weikum. 2007. Yago: a core of semantic knowledge. In Proceedings of the 16th international conference on World Wide Web, pages 697-706. ACM.
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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
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Dwivedi, V. P., Joshi, C. K., Luu, A. T., Laurent, T., Bengio, Y., & Bresson, X. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.00982 · Cited by 573 · see citing works
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Wang, B., Shen, T., Long, G., Zhou, T., Wang, Y., & Chang, Y. (2021) · International World Wide Web Conference (WWW) · doi:10.1145/3442381.3450043 · Cited by 282 · see citing works
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Peng, W., Varanka, T., Mostafa, A., Shi, H., & Zhao, G. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3136921 · Cited by 220 · see citing works
TimeTraveler: Reinforcement Learning for Temporal Knowledge Graph Forecasting
Sun, H., Zhong, J., Ma, Y., Han, Z., & He, K. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.655 · Cited by 210 · see citing works
Machine Learning on Graphs: A Model and Comprehensive TaxonomyPreprint
Chami, I., Abu-El-Haija, S., Perozzi, B., Ré, C., & Murphy, K. P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.03675 · Cited by 228 · see citing works
Self-citationDual Quaternion Knowledge Graph Embeddings
Cao, Z., Xu, Q., Yang, Z., Cao, X., & Huang, Q. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i8.16850 · Cited by 178 · see citing works
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Cao, J., Fang, J., Meng, Z., & Liang, S. (2024) · ACM Computing Surveys · doi:10.1145/3643806 · Cited by 188 · see citing works
HGCF: Hyperbolic Graph Convolution Networks for Collaborative Filtering
Sun, J., Cheng, Z., Zuberi, S., Pérez, F., & Volkovs, M. (2021) · International World Wide Web Conference (WWW) · doi:10.1145/3442381.3450101 · Cited by 130 · see citing works
HittER: Hierarchical Transformers for Knowledge Graph Embeddings
Chen, S., Liu, X., Gao, J., Jiao, J., Zhang, R., & Ji, Y. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.812 · Cited by 136 · see citing works
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Niu, G., Li, Y., Tang, C., Geng, R., Dai, J., Liu, Q., Wang, H., Sun, J., Huang, F., & Si, L. (2021) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3404835.3462925 · Cited by 100 · see citing works
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Xie, X. Y., Zhang, N., Li, Z., Deng, S., Chen, H., Xiong, F., Chen, M., & Chen, H. (2022) · International World Wide Web Conference (WWW) · doi:10.1145/3487553.3524238 · Cited by 95 · see citing works
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Zeb, A., Saif, S., Chen, J., Haq, A. U., Gong, Z., & Zhang, D. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.116796 · Cited by 72 · see citing works
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Broscheit, S., Ruffinelli, D., Kochsiek, A., Betz, P., & Gemulla, R. (2020) · Conference on Empirical Methods in Natural Language Processing: System Demonstrations (EMNLP) · doi:10.18653/v1/2020.emnlp-demos.22 · Cited by 69 · see citing works
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“While there are methods for reasoning over KGs that use hyperbolic space (MuRP [15], RotH [16]), which is suitable for modeling tree-like graphs, the choice of relational transformations used in these works (rotation) prevents them from faithfully capturing all the properties of hierarchical relations.”
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Feng, S., Zhao, K., Fang, L., Feng, K., Wei, W., Li, X., & Shao, L. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3209067 · Cited by 29 · see citing works
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Zhao, Y., Feng, H., Zhou, H., Yang, Y., Chen, X., Xie, R., Zhuang, F., & Li, Q. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107909 · Cited by 27 · see citing works
Extrapolation over temporal knowledge graph via hyperbolic embedding
Jia, Y., Lin, M., Wang, Y., Li, J., Chen, K., Siebert, J. I., Zhang, G. Z., & Liao, Q. (2023) · CAAI Transactions on Intelligence Technology · doi:10.1049/cit2.12186 · Cited by 28 · see citing works
Hypformer: Exploring Efficient Transformer Fully in Hyperbolic Space
Yang, M., Verma, H., Zhang, D. C., Liu, J., King, I., & Ying, R. (2024) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3637528.3672039 · Cited by 26 · see citing works
Research on knowledge graph alignment model based on deep learning
Yu, C., Wang, F., Liu, Y., & An, L. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115768 · Cited by 26 · see citing works
Multi-Concept Representation Learning for Knowledge Graph Completion
Wang, J., Wang, B., Gao, J., Hu, Y., & Yin, B. (2022) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3533017 · Cited by 26 · see citing works
Knowledge graph embedding with the special orthogonal group in quaternion space for link prediction
Le, T., Tran, H., & Le, B. H. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110400 · Cited by 26 · see citing works
Reasoning Through Memorization: Nearest Neighbor Knowledge Graph Embeddings
Wang, P., Xie, X. Y., Wang, X., & Zhang, N. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-44693-1_9 · Cited by 30 · see citing works
Large Language Model Enhanced Knowledge Representation Learning: A Survey
Wang, X., Chen, Z., Wang, H., Leong Hou, U., Li, Z., & Guo, W. (2025) · Data Science and Engineering · doi:10.1007/s41019-025-00285-y · Cited by 30 · see citing works
Hierarchical Neighbor Propagation With Bidirectional Graph Attention Network for Relation Prediction
Xie, Z., Zhu, R., Liu, J., Zhou, G., & Huang, J. X. (2021) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2021.3079812 · Cited by 25 · see citing works
Knowledge embedding via hyperbolic skipped graph convolutional networks
Yao, S., Pi, D., & Chen, J. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.01.037 · Cited by 25 · see citing works
Highly Efficient Knowledge Graph Embedding Learning with Orthogonal Procrustes Analysis
Peng, X., Chen, G., Lin, C., & Stevenson, M. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.187 · Cited by 24 · see citing works
Knowledge graph embedding by reflection transformation
Zhang, Q., Wang, R., Yang, J., & Xue, L. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107861 · Cited by 24 · see citing works
Poincaré Heterogeneous Graph Neural Networks for Sequential Recommendation
Guo, N., Liu, X., Li, S., Ma, Q., Gao, K., Han, B., Zheng, L., Guo, S., & Guo, X. (2023) · ACM Transactions on Information Systems · doi:10.1145/3568395 · Cited by 24 · see citing works
Multi-Level Interaction Based Knowledge Graph Completion
Wang, J., Wang, B., Gao, J., Hu, S., Hu, Y., & Yin, B. (2023) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2023.3331121 · Cited by 24 · see citing works
Joint semantics and data-driven path representation for knowledge graph reasoning
Niu, G., Li, B. H., Zhang, Y., Sheng, Y., Shi, C., Li, J., & Pu, S. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.02.011 · Cited by 23 · see citing works
Dihedron Algebraic Embeddings for Spatio-Temporal Knowledge Graph Completion
Nayyeri, M., Vahdati, S., Khan, M. T., Alam, M. M., Wenige, L., Behrend, A., & Lehmann, J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-06981-9_15 · Cited by 23 · see citing works
Shrinking Embeddings for Hyper-Relational Knowledge Graphs
Xiong, B., Nayyeri, M., Pan, S., & Staab, S. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.743 · Cited by 23 · see citing works
GreenKGC: A Lightweight Knowledge Graph Completion Method
Wang, Y. C., Ge, X., Wang, B., & Kuo, C. ‐. J. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.591 · Cited by 23 · see citing works
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Qiao, H., Feng, S., Li, X., Lin, H., Hu, H., Wei, W., & Ye, Y. (2023) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3583780.3615041 · Cited by 23 · see citing works
Mixed Geometry Message and Trainable Convolutional Attention Network for Knowledge Graph Completion
Shang, B., Zhao, Y., Liu, J., & Wang, D. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i8.28745 · Cited by 23 · see citing works
MHR: A Multi-Modal Hyperbolic Representation Framework for Fake News Detection
Feng, S., Yu, G., Liu, D., Hu, H., Luo, Y., Lin, H., & Ong, Y.-S. (2025) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2025.3528951 · Cited by 22 · see citing works
Neural Distance Embeddings for Biological SequencesPreprint
Corso, G., Ying, R., Pándy, M., Veličković, P., Leskovec, J., & Lió, P. (2021) · arXiv (Cornell University) · doi:10.17863/cam.78685 · Cited by 22 · see citing works
HGE: Embedding Temporal Knowledge Graphs in a Product Space of Heterogeneous Geometric Subspaces
Pan, J., Nayyeri, M., Li, Y., & Staab, S. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i8.28739 · Cited by 21 · see citing works
DiriE: Knowledge Graph Embedding with Dirichlet Distribution
Wang, F., Zhang, Z., Sun, L. L., Ye, J., & Yang, Y. T. (2022) · International World Wide Web Conference (WWW) · doi:10.1145/3485447.3512028 · Cited by 21 · see citing works
TDN: Triplet Distributor Network for Knowledge Graph Completion
Wang, J., Wang, B., Gao, J., Li, X., Hu, Y., & Yin, B. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3272568 · Cited by 21 · see citing works
Double-Branch Multi-Attention based Graph Neural Network for Knowledge Graph Completion
Xu, H., Bao, J., & Liu, W. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.850 · Cited by 21 · see citing works
Hu, Z., Gutiérrez-Basulto, V., Xiang, Z., Li, R., & Pan, J. Z. (2023) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3583780.3614922 · Cited by 21 · see citing works
Veena, G., Gupta, D., & Kanjirangat, V. (2023) · IEEE Access · doi:10.1109/access.2023.3339552 · Cited by 20 · see citing works
Wang, J., Cui, Z., Wang, B., Pan, S., Gao, J., Yin, B., & Gao, W. (2024) · International World Wide Web Conference (WWW) · doi:10.1145/3589334.3645361 · Cited by 20 · see citing works
Billion-scale Pre-trained E-commerce Product Knowledge Graph Model
Zhang, W., Wong, C.-M., Ye, G., Wen, B., Zhang, W., & Chen, H. (2021) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde51399.2021.00280 · Cited by 21 · see citing works
Hyperbolic disc embedding of functional human brain connectomes using resting-state fMRI
Whi, W., Ha, S., Kang, H., & Lee, D. S. (2022) · Network Neuroscience · doi:10.1162/netn_a_00243 · Cited by 19 · see citing works
Swift and Sure: Hardness-aware Contrastive Learning for Low-dimensional Knowledge Graph Embeddings
Wang, K., Liu, Y., & Sheng, Q. Z. (2022) · International World Wide Web Conference (WWW) · doi:10.1145/3485447.3511927 · Cited by 19 · see citing works
Li, X., Ma, J., Yu, J. X., Zhao, M., Yu, M., Liu, H., Ding, W., & Yu, R. (2023) · Information Sciences · doi:10.1016/j.ins.2023.01.113 · Cited by 19 · see citing works
Zhang, S., Liang, X., Li, Z., Feng, J., Zheng, X., & Wu, B. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-30672-3_45 · Cited by 19 · see citing works
κHGCN: Tree-likeness Modeling via Continuous and Discrete Curvature Learning
Yang, M., Zhou, M., Pan, L., & King, I. (2023) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3580305.3599532 · Cited by 19 · see citing works
A Lightweight Knowledge Graph Embedding Framework for Efficient Inference and Storage
Wang, H., Wang, Y., Lian, D., & Gao, J. (2021) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3459637.3482224 · Cited by 18 · see citing works
LinE: Logical Query Reasoning over Hierarchical Knowledge Graphs
Huang, Z., Chiang, M.-F., & Lee, W.-C. (2022) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3534678.3539338 · Cited by 18 · see citing works
Liu, Y., Sun, Z., Li, G., & Hu, W. P. (2022) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3511808.3557355 · Cited by 18 · see citing works
Attention-based exploitation and exploration strategy for multi-hop knowledge graph reasoning
Shang, B., Zhao, Y., Liu, Y., & Wang, C. (2023) · Information Sciences · doi:10.1016/j.ins.2023.119787 · Cited by 19 · see citing works
MADE: Multicurvature Adaptive Embedding for Temporal Knowledge Graph Completion
Wang, J., Wang, B., Gao, J., Pan, S., Liu, T., Yin, B., & Gao, W. (2024) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2024.3392957 · Cited by 19 · see citing works
Anchors-Based Incremental Embedding for Growing Knowledge Graphs
Dong, L., Zhao, D., Xiao-ai, Z., Li, X., Kang, X., & Yao, H. L. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3136482 · Cited by 17 · see citing works
Translation-Based Embeddings with Octonion for Knowledge Graph Completion
Yu, M., Bai, C., Yu, J. X., Zhao, M., Xu, T., Liu, H., Li, X., & Yu, R. (2022) · Applied Sciences · doi:10.3390/app12083935 · Cited by 17 · see citing works
HYPER2: Hyperbolic embedding for hyper-relational link prediction
Yan, S., Zhang, Z., Sun, X., Xu, G., Jin, L., & Li, S. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.04.026 · Cited by 17 · see citing works
Tran, H.-N., & Takasu, A. (2022) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2022/314 · Cited by 17 · see citing works
Multi-view semantic enhancement model for few-shot knowledge graph completion
Ma, R., Wu, H., Wang, X., Wang, W., Ma, Y., & Zhao, L. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122086 · Cited by 17 · see citing works
Incorporating Global Information in Local Attention for Knowledge Representation Learning
Zhao, Y., Zhou, H., Xie, R., Zhuang, F., Li, Q., & Liu, J. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.115 · Cited by 16 · see citing works
- Authors
- Chami, I., Wolf, A., Juan, D.-C., Sala, F., Ravi, S., & Ré, C.
- Affiliations
- Stanford University (US)
- Google (United States) (US)
- ORCID
- Ré, Christopher
- Venue
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Published
- 2020
- Pages
- 6901-6914
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W3023009950
- Cited by
- 384
- References
- 33
- Citations by year
- 2026: 26 · 2025: 55 · 2024: 87 · 2023: 78 · 2022: 59 · 2021: 69 · 2020: 10
- FWCI
- 20.63
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1763315, 1563078, 1937301)
- Gordon and Betty Moore Foundation
- Accenture
- VMware
- Okawa Foundation for Information and Telecommunications
- Swiss Re
- Ant Financial Services Group
- National Institutes of Health (N000141712266)
- Defense Advanced Research Projects Agency (FA86501827882, FA86501827865)
- Office of Naval Research (N000141712266)
- National Institute of Biomedical Imaging and Bioengineering (U54EB020405)
- 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, hyperbolic embeddings, hyperbolic geometry, hyperbolic reflections, hyperbolic rotations, attention mechanism, hierarchical relations, logical relations, low-dimensional representation, missing fact prediction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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