Temporal Knowledge Graph Completion: A Survey
Open accessAbstract
Knowledge graph completion (KGC) predicts missing links and is crucial for real-life knowledge graphs, which widely suffer from incompleteness. KGC methods assume a knowledge graph is static, but that may lead to inaccurate prediction results because many facts in the knowledge graphs change over time. Emerging methods have recently shown improved prediction results by further incorporating the temporal validity of facts; namely, temporal knowledge graph completion (TKGC). With this temporal information, TKGC methods explicitly learn the dynamic evolution of the knowledge graph that KGC methods fail to capture. In this paper, for the first time, we comprehensively summarize the recent advances in TKGC research. First, we detail the background of TKGC, including the preliminary knowledge, benchmark datasets, and evaluation metrics. Then, we summarize existing TKGC methods based on how the temporal validity of facts is used to capture the temporal dynamics. Finally, we conclude the paper and present future research directions of TKGC.
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Cites
Knowledge Graph Embedding for Link Prediction
Rossi, A., Barbosa, D., Firmani, D., Matinata, A., & Merialdo, P. (2021) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3424672 · Cited by 601 · cited 4× · see citing works
“Tensor decomposition has been effective for KGC meth-ods because it is light weighted and easy to train [Rossi et al. , 2021].”
BackgroundMethodInfluentialTeMP: 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 · see citing works
Chen, T., Wang, Z., Li, L., Li, D., Li, Z., Chang, X., Bian, P., Liang, G., Wang, Q., & Xie, T. (2025) · Schloss Dagstuhl – Leibniz-Zentrum für Informatik · doi:10.4230/lipics.ecoop.2025.6
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LLMs for knowledge graph construction and reasoning: recent capabilities and future opportunities
Zhu, Y., Wang, X., Chen, J., Qiao, S., Ou, Y., Yao, Y., Deng, S., Chen, H., & Zhang, N. (2024) · World Wide Web · doi:10.1007/s11280-024-01297-w · Cited by 294 · see citing works
“Knowledge Graph (KG) is a semantic network comprising entities, concepts, and relations (Cai et al., 2022; Chen et al., 2023; Zhu et al., 2022; Liang et al., 2022), which can catalyse applications across various scenarios such as recommendation systems, search engines, and question-answering…”
- Authors
- Cai, B., Xiang, Y., Gao, L., Zhang, H., Li, Y., & Li, J.
- Affiliations
- Deakin University (AU)
- CNPIEC KEXIN LTD
- Venue
- Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence
- Published
- 2023-08
- Pages
- 6545-6553
- OpenAlex
- W4385768178
- Cited by
- 133
- References
- 3
- Citations by year
- 2026: 31 · 2025: 33 · 2024: 39 · 2023: 20 · 2022: 10
- FWCI
- 19.52
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Joint Conference on Artificial Intelligence (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- arXiv (Cornell University)
- Funders
- Australian Research Council (LP190100594)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Semantic Web and Ontologies
- Keywords
- temporal knowledge graph completion, knowledge graph completion, missing link prediction, knowledge graph evolution, dynamic knowledge graphs, temporal knowledge graphs
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