Ontological Rules for UML-Based Conceptual Modeling
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
UML is used as a language for object-oriented software design, and as a language for conceptual modeling of applications domains. Given the differences between these purposes, UML’s origins in software engineering might limit its appropriateness for conceptual modeling. In this context, Evermann and Wand have proposed a set of well-defined ontological rules to constrain the construction of UML diagrams to reflect underlying ontological assumptions about the real world. The authors extend their work using a design research approach that examines these rules by studying the consequences of integrating them into a UML CASE tool. The paper demonstrates how design insights from incorporating theory-based modeling rules in a software artifact can be used to shed light on the rules themselves. In particular, the authors distinguish four categories of rules for implementation purposes, reflecting the relative importance of different rules and the degree of flexibility available in enforcing them. They propose distinct implementation strategies that correspond to these four rule categories and identify some redundant rules as well as some rules that cannot be implemented without changing the UML specification. The rules are implemented in an open-source UML CASE tool.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it