Bibliographic record
Abstract
Data semantic and schematic heterogeneity is a major obstacle to the reuse and sharing of geospatial data. This research focuses on developing an ontology-based methodology to logically integrate heterogeneous geographic data in a cross-border context Three main obstacles hindering data integration are semantic, schematic, and syntactic heterogeneity. Approaches to overcome these obstacles in previous research are reviewed. Among the different approaches, an ontology-based approach is selected for horizontal geospatial data integration in the context of cross-border applications. The integration methodology includes the extraction of application schemas and application ontologies, ontology integration, the creation of a reference model (or ontologies), schema matching and integration, and the creation of usable integrated datasets. The methodology is conceptual and integrates geospatial data based on the semantic content and so is not tied to specific data formats, geometric representations, or feature locations. In order to facilitate the integration procedure, four semantic relationships are used: refer-to, semantic equivalence, semantic generalization, and semantic aggregation. A hybrid ontology approach is employed in order to facilitate the addition of new geospatial data sources to the integration process. As such, three levels of ontologies are developed and illustrated within a MS ACCESS database: application, domain, and a reference model. Furthermore, a working integration prototype is designed to facilitate the integration of geospatial data in the North American context given the semantic and schema heterogeneities in international Canadian-US geospatial datasets. The methodology and prototype provide users with the ability to freely query and retrieve data without knowledge of the heterogeneous data ontologies and schemas. This is illustrated via a case study identifying critical infrastructure around the Ambassador Bridge international border crossing. The methodology and prototype are compared and evaluated with other GDI approaches and by criteria introduced by Buccella et al. (2009). Specific challenges unique to GDI were uncovered and include geographic discrepancies, scale compatibility and temporal issues.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".