An ontology-based method for quality assessment of spatial data bases
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
We propose an ontological approach for the quality assessment of spatial databases. This process is carried out at two levels. At the ontological level, the internal consistency of the specifications is considered. At the data level, real objects and their relations are studied with respect to the specifications. For this purpose, the national topographic database of Canada is selected as the case study. The ontology of the spatial database is translated into a knowledge base coded in Prolog. Then rules that define inconsistencies were defined. The querying of the knowledge base to determine the existence of such inconsistencies was carried out on a very large fact base. By this process, spatial relation between each pair of objects is analyzed with respect to the permitted relation between such objects in the ontology. The results obtained from various experimentations indicate the presence of several inconsistencies in the analyzed data set. These problems were attributed firstly to the control system that oversees the production process and secondly to the incomplete ontology. The overall approach appeared to be justified. The results obtained from several experimentation illustrated the potential of the proposed method for the quality assessment of spatial data bases in both ontological and data levels.
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 | 0.009 | 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 | 0.003 | 0.001 |
| 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