PSOATransRun: Translating and Running PSOA RuleML via the TPTP Interchange Language for Theorem Provers.
Bibliographic record
Abstract
Abstract. PSOA RuleML is an object-relational rule language general-izing POSL, OO RuleML, F-logic, and RIF-BLD. In PSOA RuleML, the notion of positional-slotted, object-applicative (psoa) terms is used as a generalization of: (1) positional-slotted terms in POSL and OO RuleML and (2) frame and class-membership terms in F-logic and RIF-BLD. We demonstrate an online PSOA RuleML reasoning service, PSOATransRun, consisting of a translator and an execution engine. The translator, PSOA2TPTP, maps knowledge bases and queries in the PSOA RuleML presentation syntax to the popular TPTP interchange language, which is supported by many first-order logic theorem provers. The trans-lated documents are then executed by the open-source VampirePrime reasoner to perform query answering. In our implementation, we use the ANTLR v3 parser generator tool to build the translator based on the grammars we developed. We wrap the translator and execution engine as resources into a RESTful Web API for convenient access. The presen-tation demonstrates PSOATransRun with a suite of examples that also constitute an online-interactive introduction to PSOA RuleML. 1
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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 | 0.006 | 0.019 |
| 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 | 0.005 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 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".