Bibliographic record
Abstract
New advances in spatial information technology have made available large amount of spatial data from different sources with different quality levels. Today, the important challenge for the scientists in geographical information sciences is how to integrate these data in order to respond to the new and emerging needs of the society for the higher spatial data quality. Modern and personalized applications of the geospatial data require efficient, interactive and on-the-fly data integration. Semantic similarity assessment plays a very important role in ontology and spatial data integration. This paper, reviews different methods for semantic similarity assessment and proposes a new logical based method in order to establish the necessary links between different ontologies. These links are then used in order to create a new ontology that will serve as basis for the integration of the spatial databases. For this study, we used the national topographic database of Canada and the topographic database of the Quebec province. Both of these databases cover the same geographical area. Most of the features in the databases are the same but, differences occur in the definitions of concepts, categories, classification, granularity and resolution, spatial relations, metric and topological constrains and etc. In this experimentation, the ontologies of the databases are formalised and represented in a knowledgebase then, a matching process proposed between the two ontologies and results were analysed in order to evaluate the proposed method for the similarity assessment of the concepts. Finally, further investigations are proposed in order to take in to account the ontology of the users in the integration process in order to guarantee the external quality of the integrated databases.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.046 | 0.025 |
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".