Temporal Dimensions of Quality in Knowledge Graph Evolution: A Comprehensive Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Efforts to enrich Knowledge Graphs (KGs) typically seek to augment data quality, semantic comprehension, and functional capabilities via the integration of various data sources.However, the inherent evolution of these sources over time potentially compromises the quality of the KGs.This paper provides a systematic exploration of the temporal challenges intrinsic to the progression of KGs, including the dynamics of changes, anomaly detection, the estimation of repair costs, and the delicate balance between changes and consistency.The complexities associated with the accurate representation of time in KGs are addressed, providing a critical assessment and understanding of this issue.A correction framework, bolstered by temporal considerations, is proposed, with an intent to scrutinize these techniques using various datasets in future research endeavors.This work represents a step forward in comprehending the quality of KGs by delving into their temporal aspects.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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