CoChat
Sign Up
Cited by 8

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

3DRTE: 3D Rotation Embedding in Temporal Knowledge Graph

Wang, Jingbin, Zhang, Wang, Chen, Xinyuan, Lei, Jing, Lai, Xiaolian·2020·IEEE Access
DOI 10.1109/access.2020.3036897
Open this paper in CoChat — free account

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1109/access.2020.3036897

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

Figures

FIGURE 1 The overall structure of 3DRTE. The relation firstly fuses its time tokens selectively, then is interpreted as a 3D Rotation (a green curve) between the head and tail entities (two black points).
FIGURE 1 The overall structure of 3DRTE. The relation firstly fuses its time tokens selectively, then is interpreted as a 3D Rotation (a green curve) between the head and tail entities (two black points).CC-BY-NC-ND · From the PDF
FIGURE 2 Token Mapping Protocol in [12].
FIGURE 2 Token Mapping Protocol in [12].CC-BY-NC-ND · From the PDF
FIGURE 3 The calculation process of our relation-time fusion. The relation (yellow) and time (blue) extracted from their own matrix are concatenated and used as input to the whole calculation diagram; after a set of operations including matrix multiplications, output is obtained (dark green).
FIGURE 3 The calculation process of our relation-time fusion. The relation (yellow) and time (blue) extracted from their own matrix are concatenated and used as input to the whole calculation diagram; after a set of operations including matrix multiplications, output is obtained (dark green).CC-BY-NC-ND · From the PDF
Image 4
CC-BY-NC-ND · From the PDF
Image 5
CC-BY-NC-ND · From the PDF
Image 6
CC-BY-NC-ND · From the PDF
Image 7
CC-BY-NC-ND · From the PDF
Image 8
CC-BY-NC-ND · From the PDF

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Research works printed but not linked: 20

  1. 10.18653/v1/D16-1260 · DOI
  2. 10.18653/v1/D18-1225 · DOI · Paper page
  3. 10.18653/v1/D18-1516 · DOI · Paper page
  4. 10.1609/aaai.v34i04.5815 · DOI · Paper page
  5. vaswani, 2017, Attention is all you need, Proc Adv Neural Inf Process Syst, 5998
  6. 10.1080/14786444408645047 · DOI
  7. boschee, 2015, ICEWS Coded Event Data Version V29
  8. leetaru, 2017, Gdelt: Global data on events, location, and tone, 1979–2012, Proc Int Conf Mach Learn (ICML), 1
  9. trivedi, 2017, Know-evolve: Deep temporal reasoning for dynamic knowledge graphs, Proc 34th Int Conf Mach Learn, 70, 3462
  10. yang, 2014, Embedding entities and relations for learning and inference in knowledge bases, arXiv 1412 6575
  11. wang, 2014, Knowledge graph embedding by translating on hyperplanes, Proc 28th AAAI Conf Artif Intell, 1112
  12. 10.1016/j.procs.2017.05.045 · DOI
  13. zhang, 2019, Quaternion knowledge graph embeddings, Proc Adv Neural Inf Process Syst, 2731
  14. ebisu, 2018, Toruse: Knowledge graph embedding on a lie group, Proc 22nd AAAI Conf Artif Intell, 1
  15. bordes, 2013, Translating embeddings for modeling multi-relational data, Proc Adv Neural Inf Process Syst, 2787
  16. 10.1109/TPAMI.2013.50 · DOI · Paper page
  17. dettmers, 2017, Convolutional 2D knowledge graph embeddings, arXiv 1707 01476
  18. parcollet, 2019, Quaternion recurrent neural networks, Proc Int Conf Learn Represent, 1
  19. glorot, 2010, Understanding the difficulty of training deep feedforward neural networks, Proc 13th Int Conf Artif Intell Statist, 249
  20. 10.1109/ICCV.2015.123 · DOI · Paper page

Cited by

Sorted by citations

See all 8

Metadata

Authors
Wang, J., Zhang, W., Chen, X., Lei, J., & Lai, X.
ORCID
Venue
IEEE Access
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2020
Volume
8
Pages
207515-207523
ISSN
2169-3536
DOI
10.1109/access.2020.3036897
Cited by
8
References
2
Licence
creativecommons.org/licenses/by-nc-nd/4.0/ (vor)
Funders
  • National Natural Science Foundation of China (61672159)
  • Industry-University-Research Collaboration in Fujian Province of China (2017H6008, 2018H6010)

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 ›

Are you an author, or do you see a problem with this page? Request a review