Learning Dynamic and Static Representations for Extrapolation-Based Temporal Knowledge Graph Reasoning
Why this work is in the frame
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Bibliographic record
Abstract
Temporal knowledge graph reasoning aims to predict the missing links (facts) in the future timestamps. However, most existing methods have a common limitation: they focus on learning dynamic representations of temporal knowledge graphs and rarely consider static characteristics that remain unchanged over time. To address the above issues, we propose to learn the dynamic and static representations for temporal knowledge graph reasoning (DSTKG), which introduces two latent variables to capture the dynamic and static characteristics of entities in temporal knowledge graphs. First, we use a Bi-GRU-based inference network to learn the static latent representation of historical facts and a nonlinear discrete-time transition-based inference network to learn the dynamic latent representation. Then, we sample the latent variables multiple times using re-parameterization tricks to obtain high-quality embeddings and make predictions in the future timestamps. The empirical results on four benchmark datasets show that our model is more effective than state-of-the-art approaches. Compared with the strong baseline model DBKGE (RotatE), the proposed model achieves performance improvements of 2.69%, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$1.59\%$</tex-math></inline-formula>, 1.18% and 1.22% on Yago11k, Wikidata12k, ICEWS14 and ICEWS05-15 respectively, regarding the evaluation metric MRR.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it