Bibliographic record
Abstract
Integrity constraints (ICs) provide a valuable tool for enforcing correct application semantics. However, designing ICs requires experts and time. Proposals for automatic discovery have been made for some formalisms, such as functional dependencies and their extension conditional functional dependencies. Unfortunately, these dependencies cannot express many common business rules. For example, an American citizen cannot have lower salary and higher tax rate than another citizen in the same state. In this paper, we tackle the challenges of discovering dependencies in a more expressive integrity constraint language, namely Denial Constraints (DCs). DCs are expressive enough to overcome the limits of previous languages and, at the same time, have enough structure to allow efficient discovery and application in several scenarios. We lay out theoretical and practical foundations for DCs, including a set of sound inference rules and a linear algorithm for implication testing. We then develop an efficient instance-driven DC discovery algorithm and propose a novel scoring function to rank DCs for user validation. Using real-world and synthetic datasets, we experimentally evaluate scalability and effectiveness of our solution.
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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 | 0.010 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".