Bibliographic record
Abstract
Abstract. Although it is widely accepted that UML could be used both for modeling software and the problem domain that is supported by an information system, its suitability for the latter in the early development phases has been questioned. Also, it is argued by many researchers that early development phases are critical for successful and cost efficient information system development. In view of this, we propose to develop an UML-like modeling language based on ontology that can be viewed as a UML core suitable for conceptual modeling. In this paper, we focus on evaluating the notions of type and role using Bunge’s ontology and OntoClean methodology, and show significant implications for our understanding on how to use types and roles in UML and conceptual modeling. These results represent the first stage in developing our ontological UML conceptual modeling core which is expected to address many issues with respect to ambiguity, inconsistency, inadequacy, and complexity in the current UML specification, and provide the foundation needed to develop better conceptual models for systems development.
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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 | 0.031 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 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".