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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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“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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“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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Clifford Embeddings – A Generalized Approach for Embedding in Normed Algebras
Demir, C., & Ngomo, A.-C. N. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43418-1_34 · Cited by 6
Zhang, C., Zhang, A., Li, Z., Yu, Y., Zhao, W., & Geng, H. (2023) · IEEE Access · doi:10.1109/access.2023.3347249 · Cited by 6
Representation of Multirelations of Geographic Scenes Based on Hyperbolic Space
Cui, W., Xu, X., Chen, J., Feng, Z., Zhao, H., Hao, Y., Tian, Y., Wang, J., & Xia, C. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3407843 · Cited by 6
Knowledge Graph Representation via Hierarchical Hyperbolic Neural Graph Embedding
Wang, S., Wei, X., dos Santos, C. N., Wang, Z., Nallapati, R. M., Arnold, A. O., & Yu, P. S. (2021) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata52589.2021.9671651 · Cited by 6
Geometric Inductive Matrix Completion
Zhang, C., Chen, H., Zhang, S., Xu, G., & Gao, J. (2022) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3488560.3498402 · Cited by 6
Knowledge Based Hyperbolic Propagation
Tai, C.-Y., Huang, C.-K., Huang, L.-Y., & Ku, L. (2021) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3404835.3462980 · Cited by 5
Knowledgebra: An Algebraic Learning Framework for Knowledge Graph
Yang, T., Wang, Y., Sha, L., Engelbrecht, J. R., & Hong, P. (2022) · Machine Learning and Knowledge Extraction · doi:10.3390/make4020019 · Cited by 6
NePTuNe: Neural Powered Tucker Networkfor Knowledge Graph Completion
Sonkar, S., Katiyar, A., & Baraniuk, R. G. (2021) · International Joint Conference on Knowledge Graphs (IJCKG) · doi:10.1145/3502223.3502249 · Cited by 5
TREPH: A Plug-In Topological Layer for Graph Neural Networks
Ye, X., Sun, F., & Xiang, S. (2023) · Entropy · doi:10.3390/e25020331 · Cited by 5
Han, H., Li, X., & Wu, K. (2023) · Applied Sciences · doi:10.3390/app13052817 · Cited by 5
A contrastive knowledge graph embedding model with hierarchical attention and dynamic completion
Shang, B., Zhao, Y., Liu, J., Liu, Y., & Wang, C. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08514-z · Cited by 5
KSGAN: Knowledge-aware subgraph attention network for scholarly community recommendation
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Integrating Knowledge Graph Embeddings and Pre-trained Language Models in Hypercomplex Spaces
Nayyeri, M., Wang, Z., Akter, M. M., Alam, M. M., Rony, M. R. A. H., Lehmann, J., & Staab, S. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-47240-4_21 · Cited by 5
Relation and Entropy Weight-Aware Knowledge Graph Embedding for Cloud Manufacturing
Zhao, F., Jin, L., Yang, L. T., & Jin, H. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3178414 · Cited by 5
Event Temporal Relation Extraction with Bayesian Translational Model
Tan, X., Pergola, G., & He, Y. (2023) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2023.eacl-main.80 · Cited by 6
KACC: A Multi-task Benchmark for Knowledge Abstraction, Concretization and Completion
Zhou, J., Hu, S., Lv, X., Yang, C. H., Liu, Z., Xu, W. H., Jiang, J., Li, J., & Sun, M. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.153 · Cited by 6
Semantic Triple Encoder for Fast Open-Set Link Prediction.Preprint
Wang, B., Shen, T., Long, G., Zhou, T., & Chang, Y. (2020) · arXiv (Cornell University) · Cited by 5
Directed Graph Embeddings in Pseudo-Riemannian ManifoldsPreprint
Sim, A., Wiatrak, M., Brayne, A., Creed, P., & Paliwal, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.08678 · Cited by 5
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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