Verifying and Mapping the Mereotopology of Upper-Level Ontologies
Bibliographic record
Abstract
Upper-level ontologies provide an account of the most basic, domain-independent, existing entities, such as time, space, objects, and processes. Ontology verification is the process by which a theory is checked to rule out unintended models, and possibly characterize missing intended ones. In this paper, we verify the core characterization of mereotopology of the Suggested Upper Merged Ontology (SUMO), and the mereology of the Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE), while relating their axiomatizations via ontology mapping. As a result, we propose the correction and addition of some axioms to the analyzed theories which eliminate unintended models and characterize missing ones. In addition, we show by formal means which is the relation existing between the axiomatization of mereology in both upper-level ontologies, and make available a modular representation in first-order logic of the SUMO characterization of mereotopology.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".