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Low-Dimensional Hyperbolic Knowledge Graph Embeddings
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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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“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]
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In the citing paper
“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]
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“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]
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“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]
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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
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
Veena, G., Gupta, D., & Kanjirangat, V. (2023) · IEEE Access · doi:10.1109/access.2023.3339552 · Cited by 21
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
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 22
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 20
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
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 20
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
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
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
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
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 24
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
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
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
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 19
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
Tran, H.-N., & Takasu, A. (2022) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2022/314 · Cited by 20
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 18
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
Metadata
- 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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