Attentional Neural Integral Equation for Temporal Knowledge Graph Forecasting
Bibliographic record
Abstract
Temporal Knowledge Graph Forecasting (TKGF) aims to forecast the missing entities or relations at a specific timestamp when only the historical information is observed. It is crucial to accurately identify the historical information of complex temporal relational graphs related to the query. Existing works, e.g., TANGO, have exploited the Neural Ordinary Differential Equation (NODE) to TKGF. However, TANGO encounters two limitations. First, TANGO observes historical facts with only one timestamp at each step, leading to a long-term forgetting problem. Second, TANGO gives the same weight to the entire history graph, including facts that are not relevant to the query. To tackle the above limitations, this paper utilizes Attentional Neural Integral Equation for TKGF (tIE), enabling the global interaction between query-related historical graph sequences. To achieve this, we employ the Relational Graph Convolutional Network and Fourier-type Transformer to model the graph structure and temporal evolution of TKG. The Iterative Integral Equation Solver is exploited to enhance the accuracy and robustness of numerical solutions. The proposed method outperforms baseline models regarding several metrics and inference speed on four benchmark datasets, especially on the long horizontal link forecasting task with irregular time intervals.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".