The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Leveraging Static Models for Link Prediction in Temporal Knowledge Graphs
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The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“while M erging iteratively merges time intervals (starting from the shortest time intervals) if related facts are not violated.”
From Temporal Knowledge Graph Completion: A Survey (2023) · cites this paper as Radstok and Chekol, 2021
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“To address this, [Radstok and Chekol, 2021] discovers optimal timestamps to concatenate relations by Splitting or M erging the existing time intervals.”
From Temporal Knowledge Graph Completion: A Survey (2023) · cites this paper as Radstok and Chekol, 2021
Experimental — accuracy not yet measured
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Citations
Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
A Survey on Knowledge Graphs: Representation, Acquisition, and Applications
Ji, S., Pan, S., Cambria, E., Marttinen, P., & Yu, P. S. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3070843 · Cited by 2,947
Complex Embeddings for Simple Link Prediction
Trouillon, T., Welbl, J., Riedel, S., Gaussier, É., & Bouchard, G. (2016) · arXiv · doi:10.48550/arxiv.1606.06357 · Cited by 2,100
RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space
Sun, Z., Deng, Z.-H., Nie, J.-Y., & Tang, J. (2019) · arXiv · doi:10.48550/arxiv.1902.10197 · Cited by 1,414
SimplE Embedding for Link Prediction in Knowledge Graphs
Kazemi, S. M., & Poole, D. (2018) · arXiv · doi:10.48550/arxiv.1802.04868 · Cited by 476 · cited 3×
“However, [15] propose a simple enhancement to CP that addresses this independence, called SimplE.”
Method
Research works printed but not linked: 18
- 10.1109/TKDE.2020.3028705 · DOI
- 10.1145/3289600.3290956 · DOI
- 10.18653/v1/D16-1260 · DOI
- 10.18653/v1/2020.emnlp-main.541 · DOI
- lacroix, 2020, Tensor decompositions for temporal knowledge base completion, ICLR 2020
- 10.1145/3184558.3191639 · DOI · Paper page
- van der maaten, 2008, Visualizing data using t-sne, Journal of Machine Learning Research, 9, 2579
- bordes, 2013, Translating embeddings for modeling multi-relational data, NeurIPS 2013, 2787
- 10.18653/v1/D18-1225 · DOI · Paper page
- garreau, 2017, Change-point detection and kernel methods, These
- 10.18653/v1/D18-1516 · DOI · Paper page
- 10.1007/s10115-016-0987-z · DOI
- 10.1016/S0378-8733(03)00009-1 · DOI
- 10.1609/aaai.v34i04.5815 · DOI · Paper page
- 10.1109/JPROC.2015.2483592 · DOI
- 10.1109/ACCESS.2020.2964028 · DOI · Paper page
- 10.1016/j.sigpro.2019.107299 · DOI
- xu, 2019, Temporal knowledge graph embedding model based on additive time series decomposition, CoRR
Cited by
Sorted by citations
Temporal Knowledge Graph Completion: A Survey
Cai, B., Xiang, Y., Gao, L., Zhang, H., Li, Y., & Li, J. (2023) · Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/734 · Cited by 133
Metadata
- Authors
- Radstok, W., Chekol, M., & Velegrakis, Y.
- Affiliations
- Utrecht University,Data Intensive Systems Group,Utrecht,The Netherlands
- Venue
- 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI)
- Publisher
- IEEE
- Published
- 2021-11
- Pages
- 1034-1041
- Cited by
- 9
- References
- 5
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