Toward an Architecture for Enhancing Semantic Interoperability Based on Enrichment of Geospatial Data Semantics
Bibliographic record
Abstract
Semantic interoperability is needed to support meaningful data exchanges in distributed environments such as ad hoc networks of geospatial databases and geospatial web services. Even with the increasing popularity of ontologies to capture semantics, semantics of geospatial data are often too weak to support meaningful exchanges. In this chapter, the authors argue that semantically weak geospatial data can be enriched to enhance semantic interoperability. They propose a conceptual architecture designed to support enhanced semantic interoperability in dynamic networks that focuses on semantic enrichment. The proposed conceptual architecture includes a coalition management module, an ontology enrichment module, and a semantic mapping module; the modules perform different types of semantic enrichment and can support various semantic interoperability tasks. Within the different enrichment methods, the authors explain the role of global ontologies, arguing that they play a key role in a semantic interoperability framework. Finally, the authors illustrate with an application example the possibilities of such architecture.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".