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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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“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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Metadata
- 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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