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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Recent temporal knowledge-graph-based anomaly-detection efforts provide powerful tools to model and detect changes in time-varying relational data.
“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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Learning Meta-Representations of One-shot Relations for Temporal Knowledge Graph Link Prediction
Ding, Z., He, B., Wu, J., Ma, Y., Han, Z., & Tresp, V. (2023) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn54540.2023.10191619 · Cited by 10 · see citing works
Self-citationReasoning beyond Triples: Recent Advances in Knowledge Graph Embeddings
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ChronoR: Rotation Based Temporal Knowledge Graph Embedding
Sadeghian, A., Armandpour, M., Colas, A., & Wang, D. Z. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i7.16802 · Cited by 9 · see citing works
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Leveraging semantic property for temporal knowledge graph completion
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Out-of-Distribution Generalized Dynamic Graph Neural Network for Human Albumin Prediction
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Toward Context-Aware Anomaly Detection for AIOps in Microservices Using Dynamic Knowledge Graphs
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Temporal knowledge graph diffusion model for open-world reasoning
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NDCN-Brain: An Extensible Dynamic Functional Brain Network Model
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- 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
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 ›
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