Ontology–based Representation of Simulation Models
Bibliographic record
Abstract
Abstract—Ontologies have been used in a variety of domains for multiple purposes such as establishing common terminology, organizing domain knowledge and describing domain in a machine-readable form. Moreover, ontologies are the foundation of the Semantic Web and often semantic integration is achieved using ontology. Even though simulation demonstrates a number of similar characteristics to Semantic Web or semantic integration, including heterogeneity in the simulation domain, representation and semantics, the application of ontology in the simulation domain is still in its infancy. This paper proposes an ontology-based representation of simulation models. The goal of this research is to facilitate comparison among simulation models, querying, making inferences and reuse of existing simulation models. Specifically, such models represented in the domain simulation engine environment serve as an information source for their representation as instances of an ontology. Therefore, the ontology-based representation is created from existing simulation models in their proprietary file formats, consequently eliminating the need to perform the simulation modeling directly in the ontology. The proposed approach is evaluated on a case study involving the I2Sim interdependency simulator.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".