An Ontology for Autonomic License 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
The license agreement can be seen as the knowledge source for a license management system. As such, it may be referenced by the system each time a new process is initiated. To facilitate access, a machine readable representation of the license agreement is highly desirable, but at the same time we do not want to sacrifice too much readability of such agreements by human beings. Creating an ontology as a formal knowledge representation of licensing not only meets the representation requirements, but also offers improvements to knowledge reusability owing to the inherent sharing nature of such representations. Furthermore, the XML-based ontology languages such as OWL (Web Ontology Language) can be user friendly for the non-developers who are often those responsible for implementing and managing such license agreements. This paper shows our use of ontology to represent the license agreement in a development prototype. The ultimate goal is to build ontology for the license management domain that will facilitate autonomic knowledge management. Knowledge based on such ontology can then be shared and utilized by many types of license management system.
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 | 0.000 |
| 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 | 0.001 |
| 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