Toward formalizing domain modeling semantics in language syntax
Bibliographic record
Abstract
Information systems are situated in and are representations of some business or organizational domain. Hence, understanding the application domain is critical to the success of information systems development. To support domain understanding, the application domain is represented in conceptual models. The correctness of conceptual models can affect the development outcome and prevent costly rework during later development stages. This paper proposes a method to restrict the syntax of a modeling language to ensure that only possible configurations of a domain can be modeled, thus increasing the likelihood of creating correct domain models. The proposed method, based on domain ontologies, captures relationships among domain elements via constraints on the language metamodel, thus restricting the set of statements about the domain that can be generated with the language. In effect, this method creates domain specific modeling languages from more generic ones. The method is demonstrated using the Unified Modeling Language (UML). Specifically, it is applied to the subset of UML dealing with object behavior and its applicability is demonstrated on a specific modeling example.
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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 | 0.019 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.012 | 0.023 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".