The DOI matches its Crossref record; 39 of the 43 references it cites were found in a registry or an index.
TeMP: Temporal Message Passing for Temporal Knowledge Graph Completion
Open accessAbstract
Inferring missing facts in temporal knowledge graphs (TKGs) is a fundamental and challenging task.Previous works have approached this problem by augmenting methods for static knowledge graphs to leverage time-dependent representations.However, these methods do not explicitly leverage multi-hop structural information and temporal facts from recent time steps to enhance their predictions.Additionally, prior work does not explicitly address the temporal sparsity and variability of entity distributions in TKGs.We propose the Temporal Message Passing (TeMP) framework to address these challenges by combining graph neural networks, temporal dynamics models, data imputation and frequency-based gating techniques.Experiments 1 on standard TKG tasks show that our approach provides substantial gains compared to the previous state of the art, achieving a 10.7% average relative improvement in Hits@10 across three standard benchmarks.Our analysis also reveals important sources of variability both within and across TKG datasets, and we introduce several simple but strong baselines that outperform the prior state of the art in certain settings.
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“This graph format is closely related to the group of temporal KGs and recent temporal KG-based anomalydetection efforts provide powerful tools to model and detect changes in these time-varying relational data [31], [32], [33].”
From Toward Context-Aware Anomaly Detection for AIOps in Microservices Using Dynamic Knowledge Graphs (2026) · cites this paper as [32]
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Self-citationComplex Embeddings for Simple Link Prediction
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A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multi-Modal
Liang, K., Meng, L., Liu, M., Liu, Y., Tu, W., Wang, S., Zhou, S., Liu, X., Sun, F., & He, K. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3417451 · Cited by 438
Foundations and Modeling of Dynamic Networks Using Dynamic Graph Neural Networks: A Survey
Skarding, J., Gabryś, B., & Musiał, K. (2021) · IEEE Access · doi:10.1109/access.2021.3082932 · Cited by 382
Graph Neural Networks for Natural Language Processing: A Survey
Wu, L., Chen, Y., Shen, K., Guo, X., Gao, H., Li, S., Pei, J., & Long, B. (2023) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000096 · Cited by 417
A Survey on Knowledge Graph Embeddings for Link Prediction
Wang, M., Qiu, L., & Wang, X. (2021) · Symmetry · doi:10.3390/sym13030485 · Cited by 212
TimeTraveler: Reinforcement Learning for Temporal Knowledge Graph Forecasting
Sun, H., Zhong, J., Ma, Y., Han, Z., & He, K. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.655 · Cited by 238
Metadata
- Authors
- Wu, J., Cao, M., Cheung, J. C. K., & Hamilton, W. L.
- Affiliations
- Mila - Quebec Artificial Intelligence Institute (CA)
- Venue
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Published
- 2020
- Pages
- 5730-5746
- OpenAlex
- W3099845049
- Cited by
- 167
- References
- 43
- Citations by year
- 2026: 23 · 2025: 31 · 2024: 39 · 2023: 39 · 2022: 24 · 2021: 11
- FWCI
- 7.07
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Canadian Institute for Advanced Research
- Compute Canada
- Samsung
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Keywords
- temporal knowledge graphs, knowledge graph completion, graph neural networks, missing data imputation, temporal sparsity
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