Charting Possible Worlds: The Quest for Meaning in Ontologies
Bibliographic record
Abstract
We explore the concept of meaning in applied ontologies using possible world semantics. We begin by analyzing the nature of possible worlds in the influential framework proposed by Guarino, Oberle and Staab: interpreting an ontology involves selecting, from the set of logically possible worlds, a subset of worlds that are metaphysically possible according to the ontology – ideally corresponding to the set of worlds that are metaphysically possible according to the ontology’s creator. We argue that this framework is limited to analytic statements and should be extended to encompass synthetic statements. The analytic/synthetic distinction, we suggest, can itself be understood in terms of the necessary/contingent distinction using metaphysically possible worlds. We propose a dual framework for introducing terms in ontology development, integrating both descriptivism and Kripke’s theory of rigid designators. This framework accommodates a posteriori analytic statements, implying that the meaning of a term may be unknown even to its creator. Finally, we distinguish two distinct roles that labels can play in ontology development: either as rigid designators that carry semantic weight, or as mere human-readable tags serving as proxies for underlying descriptions.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.027 |
| Scholarly communication | 0.010 | 0.024 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".