Consistent query answering under inclusion dependencies
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
For several reasons a database may not satisfy certain integrity constraints (ICs), for example, when it is the result of integrating several independent data sources. However, most likely most of the information in it is still consistent with the ICs; and could be retrieved when queries are answered. Consistent answers with respect to a set of ICs have been characterized as answers that can be obtained from every possible minimal repair of the database. In this paper we show and analyze how specify those repairs using disjunctive logic program with a stable model semantics in the presence of referential ICs. In this case, repairs are obtained by introduction of null values that do not propagate through other constraints, which makes the problem of consistent query answering decidable. We also present results about cases where the implementation of consistent query answering can be made more efficient due to the fact that the program can be simplified into a non-disjunctive program. Finally, we discuss several research issues around the implementation of system for retrieving consistent answers to queries from a DBMS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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