Semantic mashups for simulation as a service with tag mining and ontology learning
Bibliographic record
Abstract
Nowadays, there is a trend for delivering the Simulation as a Service using web-based/cloud-based services. Existing simulation services cannot be easily discovered and composed. Although semantic mashups have become popular for implementing service composition in the Web 2.0, there are yet no semantic mashups applications focusing on modeling and simulation. Here, we propose the first existing layered architecture based on semantic mashups improving the composition of Simulation as a Service. Besides, we propose using ontology learning and tagging systems to avoid pre-defined ontology efforts and to increase the automation of composition through user participation. The general idea is to mine tag signatures from the user-interested simulation-related services automatically, to generate a tag ontology tree from the mined tag signatures automatically, and then to compose the services based on the learnt tag tree ontology. This unique approach for simulation services mashups can boost the reusability, integration, interoperability of Simulation as a Service.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".