Generating Relational Database using Ontology Review
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
A huge amount of data is being generated every day from different sources. Access to these data can be very valuable for decision-making. Nevertheless, the extraction of information of interest remains a major challenge given a large number of heterogeneous databases. Building shareable and (re)usable data access mechanisms including automated verification and inference mechanisms for knowledge discovery needs to use a common knowledge model with a secure, coherent, and efficient database. For this purpose, an ontology provides an interesting knowledge model and a relational database provides an interesting storage solution. Many papers propose methods for converting ontology to a relational database. This paper describes issues, challenges, and trends derived from the evaluation of 10 methods using 23 criteria. Following this study, this paper shows that none of the methods are complete as well as the conversion process does not use the full expressivity of ontology to derive a complete relational schema including advanced constraints and modification procedures. Thus, more work must be done to decrease the gap between ontologies, a relation database.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 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