What Can Ontologists Learn from Knowledge Management?
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
At present the development of ontologies is very much an art rather than a science. With the lack of consensual guidelines, or generic process model for ontology engineering (OE) methodologies, each development team follows its own set of principles, design criteria and stages. This situation hinders the development of shared understanding within and between teams, the extension of a given ontology by others and its reuse in other ontologies and final applications. In fact, several authors have indicated that the source of many problems challenging ontologists is the lack of such a model. To address this issue ontology engineering process model is proposed based on the premise that ontology development is a special case of Nonaka's knowledge creation model. According to the proposed model, ontology engineering process is viewed as spiraling process in which the continuous and dynamic interaction between tacit knowledge and explicit knowledge of ontology's stakeholders plays a crucial role.
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 | 0.001 | 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 | 0.001 | 0.005 |
| 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 it