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Deriving Validity Time in Knowledge Graph
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TKGC methods aim at more accurate link prediction by incorporating timestamps.
“temporal knowledge graph completion (TKGC) methods [Leblay and Chekol, 2018] start emerging, aiming at more accurate link prediction by addressing the above limitation of KGC methods.”
From Temporal Knowledge Graph Completion: A Survey (2023) · cites this paper as Leblay and Chekol, 2018
Section 2.3.1 describes time-aware knowledge graph completion that predicts an entity or relation given a time point, supporting the timestamp-based aspect of the claim. But the Conclusion identifies this paper’s main task as temporal scope prediction and says support for time-aware link prediction is planned future work; it does not establish improved link-prediction accuracy. (LLM, conf=0.84)
Experimental — accuracy not yet measuredSynthetic relations are initially adopted by [Leblay and Chekol, 2018] for factual scoring measurement.
“Synthetic relations are initially adopted by [Leblay and Chekol, 2018] for the factual scoring measurement”
From Temporal Knowledge Graph Completion: A Survey (2023) · cites this paper as Leblay and Chekol, 2018
Section 3.1.1 (TTransE) uses synthetic relations in a scoring function for temporal facts, supporting the claim that Leblay and Chekol adopted them for fact scoring. The paper does not establish that they were the first to do so, as “initially” could imply. (LLM, conf=0.82)
Experimental — accuracy not yet measuredRelation prediction and time prediction are included in some existing works, with the same metrics for accuracy measurement.
“In addition to entity prediction, relation prediction ({h, ?, t, τ } [Li et al., 2021b]) and time prediction ({h, r, t, ?} [Leblay and Chekol, 2018]) are further included in some existing works, with same metrics for the accuracy measurement.”
From Temporal Knowledge Graph Completion: A Survey (2023) · cites this paper as Leblay and Chekol, 2018
[Leblay and Chekol, 2018] evaluates predictive performance of three basic fusion functions and experimental results show u(r,τ)=e_r+e_τ is the most effective.
“It evaluates the predictive performance of three basic fusion functions of u(r, τ ), i.e., e r:τ , e r + e τ and p τ e r (p τ ∈ (0, 1] is a learnable coefficient), and the experimental results show that u(r, τ ) = e r + e τ is the most effective.”
From Temporal Knowledge Graph Completion: A Survey (2023) · cites this paper as Leblay and Chekol, 2018
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Cites
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Cited by
Sorted by citations
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
A review: Knowledge reasoning over knowledge graph
Chen, X., Jia, S., & Xiang, Y. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.112948 · Cited by 1,035
TeMP: Temporal Message Passing for Temporal Knowledge Graph Completion
Wu, J., Cao, M., Cheung, J. C. K., & Hamilton, W. L. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.462 · Cited by 167
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
A Survey on Temporal Knowledge Graph: Representation Learning and Applications
Cai, L., Mao, X., Zhou, Y., Long, Z., Wu, C., & Lan, M. (2024) · arXiv · doi:10.48550/arxiv.2403.04782 · Cited by 68
Chen, Q., & Chen, L. (2026) · Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1 · doi:10.1145/3770854.3780173
Metadata
- Authors
- Leblay, J., & Chekol, M. W.
- Affiliations
- AIST Tokyo Waterfront, Tokyo, Japan
- University of Mannheim, Mannheim, Germany
- Venue
- Companion of the The Web Conference 2018 on The Web Conference 2018 - WWW '18
- Publisher
- ACM Press
- Published
- 2018
- Pages
- 1771-1776
- Cited by
- 365
- References
- 1
- Funders
- KAKENHI (17K12786)
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