Waiting for the Chase To Terminate: Have You Tried This Other Variant?
Bibliographic record
Abstract
Ever dreamed of expanding your database with new facts derived from meta-knowledge of your domain of application? The chase algorithm will take this additional knowledge into account to figure out more complete and relevant answers to your queries! Input your favorite existential rules and dataset, sit comfortably, and watch the chase as it operates its magic!* Summary:00:00 Introduction & Intuition02:45 Framework & Definitions05:54 Fairness08:16 Termination & The Restricted Chase10:54 The Core Chase13:42 Recap' & Conclusion14:39 Credits Python/Manim code to generate the animations:github.com/KRVideosFanAccount/WaitingForTheChaseToTerminate.git Manim:www.manim.community KR website:kr.org References: Jean-François Baget, Michel Leclère, Marie-Laure Mugnier, and Éric Salvat. On rules with existential variables: Walking the decidability line. Artificial Intelligence, 175(9-10):1620–1654, 2011. Catriel Beeri and Moshe Y. Vardi. The implication problem for data dependencies. In Proceedings of the 8th Colloquium on Automata, Languages and Programming, pages 73–85, 1981. David Carral, Lucas Larroque, Marie-Laure Mugnier, and Michaël Thomazo. Normalisations of Existential Rules: Not so Innocuous! In Proceedings of the 19th International Conference on Principles of Knowledge Representation and Reasoning (KR), pages 102–111, 2022. *Disclaimer: This algorithm may take a while; please read Terms and Conditions before use. Excessive waiting for termination is dangerous for your health, run the chase with moderation.
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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 | 0.012 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.021 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.029 | 0.013 |
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".