Consistency Checking of Conceptual Models via Model Merging
Bibliographic record
Abstract
Requirements elicitation involves the construction of large sets of conceptual models. An important step in the analysis of these models is checking their consistency. Existing research largely focuses on checking consistency of individual models and of relationships between pairs of models. However, such strategy does not guarantee global consistency. In this paper, we propose a consistency checking approach that addresses this problem for homogeneous models. Given a set of models and a set of relationships between them, our approach works by first constructing a merged model and then verifying this model against the consistency constraints of interest. By keeping proper traceability information, consistency diagnostics obtained over the merge are projected back to the original models and their relationships. The paper also presents a set of reusable expressions for defining consistency constraints in conceptual modelling. We demonstrate the use of the developed expressions in the specification of consistency rules for class and ER diagrams, and i* goal models.
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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 | 0.028 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".