ENHANCING OWL ONTOLOGIES WITH RELATION SEMANTICS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The OWL Enhance prototype has been developed to augment ontologies implemented using the Web Ontology Language (OWL) with richer relation semantics. This prototype interactively elicits knowledge from providers to describe the intrinsic nature of relations and appends these elicited semantics to definitions of relations in OWL ontologies. Benefits from the explicit specification of the intrinsic nature of relations in ontologies include the development of quantitative techniques for the estimation of similarities among relations and attribute exploration techniques to create relation taxonomies. Examples of these techniques have been implemented in modules of the OWL Enhance prototype to demonstrate the utility of explicit relation semantics. Results from testing these modules on high-level and domain-specific ontologies are presented and analyzed with respect to the potential use of relation semantics to increase the fidelity of knowledge representation, as well as the potential for reuse and interoperability of knowledge on the Semantic Web.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 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 it