STransE: a novel embedding model of entities and relationships in knowledge bases
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Dat Quoc Nguyen, Kairit Sirts, Lizhen Qu, Mark Johnson. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2016.
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TransE and its variants FTransE and STransE are not fully expressive because of contradictions they impose between different relation types.
“They further prove other models are not fully expressive: DistMult, because it cannot model asymmetric relations; and transitive models such as TransE (Bordes et al., 2013) and its variants FTransE (Feng et al., 2016) and STransE (Nguyen et al., 2016), because of certain contradictions that they impose between different relation types.”
From TuckER: Tensor Factorization for Knowledge Graph Completion (2019) · cites this paper as Nguyen et al., 2016
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Duality of Link Prediction and Entailment Graph Induction
Hosseini, M. J., Cohen, S. B., Johnson, M., & Steedman, M. J. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1468 · Cited by 18 · see citing works
Self-citationTowards Temporal Knowledge Graph Embeddings with Arbitrary Time Precision
Leblay, J., Chekol, M. W., & Liu, X. (2020) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3340531.3412028 · Cited by 18 · see citing works
Binarized Embeddings for Fast, Space-Efficient Knowledge Graph Completion
Hayashi, K., Kishimoto, K., & Shimbo, M. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3075070 · Cited by 18 · see citing works
A Survey on Knowledge Graph Embedding
Yan, Q., Fan, J., Li, M., Qu, G., & Xiao, Y. (2022) · IEEE International Conference on Data Science in Cyberspace · doi:10.1109/dsc55868.2022.00086 · Cited by 18 · see citing works
Urban Multi-Source Spatio-Temporal Data Analysis Aware Knowledge Graph Embedding
Zhao, L. D., Deng, H., Qiu, L., Li, S., Hou, Z., Sun, H., & Chen, Y. (2020) · Symmetry · doi:10.3390/sym12020199 · Cited by 18 · 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
A Quaternion-Embedded Capsule Network Model for Knowledge Graph Completion
Chen, H., Wang, W., Li, G., & Shi, Y. (2020) · IEEE Access · doi:10.1109/access.2020.2997177 · Cited by 17 · see citing works
HyperKG: Hyperbolic Knowledge Graph Embeddings for Knowledge Base CompletionPreprint
Kolyvakis, P., Kalousis, A., & Kiritsis, D. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.04895 · Cited by 17 · see citing works
Interpretable and Compositional Relation Learning by Joint Training with an Autoencoder
Takahashi, R., Tian, R., & Inui, K. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1200 · Cited by 16 · see citing works
Improving Knowledge Graph Embedding Using Locally and Globally Attentive Relation Paths
Jia, N., Cheng, X., & Su, S. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-45439-5_2 · Cited by 16 · see citing works
Towards Robust Knowledge Graph Embedding via Multi-Task Reinforcement Learning
Zhang, Z., Zhuang, F., Zhu, H., Li, C., Xiong, H., He, Q., & Xu, Y. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3127951 · Cited by 15 · see citing works
Entity Embeddings with Conceptual Subspaces as a Basis for Plausible Reasoning
Jameel, S., & Schockaert, S. (2016) · ORCA Online Research @Cardiff (Cardiff University) · doi:10.48550/arxiv.1602.05765 · Cited by 15 · see citing works
BDRI: block decomposition based on relational interaction for knowledge graph completion
Yu, M., Guo, J., Yu, J. X., Xu, T., Zhao, M., Liu, H., Li, X., & Yu, R. (2023) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-023-00918-8 · Cited by 12 · see citing works
Characterizing Interactions and Relationships between People
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Learning Translation-Based Knowledge Graph Embeddings by N-Pair Translation Loss
Song, H.-J., Kim, A.-Y., & Park, S.-B. (2020) · Applied Sciences · doi:10.3390/app10113964 · Cited by 12 · see citing works
An Overview of Utilizing Knowledge Bases in Neural Networks for Question Answering
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HapPenIng: Happen, Predict, Infer—Event Series Completion in a Knowledge Graph
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A Temporal Knowledge Graph Embedding Model Based on Variable Translation
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Learning to Complete Knowledge Graphs with Deep Sequential Models
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Knowledge Graph Embedding With Interactive Guidance From Entity Descriptions
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Implicit ReasoNet: Modeling Large-Scale Structured Relationships with Shared MemoryPreprint
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Simplified Representation Learning Model Based on Parameter-Sharing for Knowledge Graph Completion
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MDE: Multiple Distance Embeddings for Link Prediction in Knowledge GraphsPreprint
Sadeghi, A., Graux, D., Yazdi, H. S., & Lehmann, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.10702 · Cited by 9 · see citing works
Zero-Shot Scene Graph Generation with Knowledge Graph Completion
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Entity Embeddings with Conceptual Subspaces as a Basis for Plausible Reasoning
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Link Prediction based on Tensor Decomposition for the Knowledge Graph of COVID-19 Antiviral Drug
Jia, T., Yang, Y., Lu, X., Zhu, Q., Yang, K., & Zhou, X. (2022) · Data Intelligence · doi:10.1162/dint_a_00117 · Cited by 8 · see citing works
Knowledge graph embedding with self adaptive double-limited loss
Zou, X., Wang, X., Cen, S., Dai, G., & Liu, C. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.109310 · Cited by 8 · see citing works
Entity-Relation Guided Random Walk for Link Prediction in Knowledge Graphs
Li, W., Zhong, H., Lin, R., Chang, C., Pan, Z., & Tang, Y. Z. (2024) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2024.3382263 · Cited by 7 · see citing works
- Authors
- Nguyen, D. Q., Sirts, K., Qu, L., & Johnson, M.
- Affiliations
- Macquarie University (AU)
- Data61 (AU)
- ORCID
- Johnson, Mark
- Venue
- Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Published
- 2016
- Pages
- 460-466
- OpenAlex
- W2463781041
- Cited by
- 177
- References
- 0
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 9 · 2023: 13 · 2022: 19 · 2021: 24 · 2020: 27 · 2019: 36 · 2018: 23 · 2017: 15 · 2016: 4
- FWCI
- 14.01
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Australian Government
- Australian Research Council (DP160102156)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Semantic Web and Ontologies
- Keywords
- knowledge base embedding, entity embeddings, knowledge graphs
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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