FOL RuleML: The First-Order Logic Web Langague
Bibliographic record
Abstract
This paper describes First-Order Logic RuleML (FOL RuleML), which is planned to be the FOL sublanguage of RuleML 0.9, the rule component of SWRL FOL, and an FOL content language for SWSI. FOL RuleML is based on a modular combination of two syntactically characterized sublanguages: (1) Quantifier RuleML extends RuleML 0.87 by explicit quantifiers. (2) Disjunctive RuleML extends RuleML 0.87 by head disjunctions. Connectives for equivalence and negation are then modularly added for defining FOL RuleML. Its DTD is available for validation tests. Classical FOL model theory provides the semantics of FOL RuleML. FOL RuleML formulas can be used as the declarative content of KQML-like performatives 'Assert' and 'Query', which are augmented by a neutral 'Consider' performative.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.014 |
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".