Bibliographic record
Abstract
The reuse of existing models in modeling and simulation software is becoming increasingly important. One way to improve model reuse is through composition. Even though much work has been done for model composition at the syntactic level, research at the semantic level is still under development. In particular, selection for model composition based on semantics is complex, because it is hard for casual users to understand the formal definition of models. Likewise, there are no automated mechanisms to build domain-specific ontologies based on the models. In order to deal with the above issues, we introduced SAMSaaS (Semantic Architecture for Modeling and Simulation as a Service), a layered architecture to deploy and compose M&S resources as services. Here, we focus on the upper layers of the architecture, and present a tag mining method and a tag-tree ontology learning approach to learn domain specific ontology for the models. We show an application to DEVS examples to show its success and possible applications.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".