Mapping and Verification of the Time Ontology in SUMO
Bibliographic record
Abstract
Many software systems rely on ontologies for semantic interoperation. However, ontologies which admit unintended models might cause misunderstandings that hinder interoperability because their vocabularies are ambiguously defined. Foundational ontologies, such as SUMO, provide rich characterizations for general concepts that underly every knowledge representation enterprise. Those ontologies are intended to be broadly reused as a reference for semantics. Ontology verification is the process by which a theory is checked to rule out unintended models by means of further axiomatization, and characterize missing intended ones. In this paper, we verify the subtheory of core temporal concepts of the SUMO foundational ontology and relate its axiomatization via ontology mapping with other time ontologies, the foundational ontology DOLCE, and the generic ontology PSL. As a result, we propose the addition of some missing axioms that we have identified during our verification task, and the correction of others.
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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 | 0.000 |
| Open science | 0.001 | 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".