An ontology-based method for quality assessment of spatial data bases
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".