A logical foundation for deductive object-oriented databases
Bibliographic record
Abstract
Over the past decade, a large number of deductive object-oriented database languages have been proposed. The earliest of these languages had few object-oriented features, and more and more features have systematically been incorporated in successive languages. However, a language with a clean logical semantics that naturally accounts for all the key object-oriented features, is still missing from the literature. Two features that are currently missing are the encapsulation of rule-based methods in classes, and nonmonotonic behavioral inheritance with overriding, conflict resolution and blocking. This paper introduces the syntax of a language with these features. It then defines a class of databases, called well-defined databases, that have an intuitive meaning and develops a direct logical semantics for this class of databases. The semantics is based on the well-founded semantics from logic programming. The work presented in this paper establishes a firm logical foundation for deductive object-oriented databases.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".