An experimental examination of property precedence in conceptual modelling
Bibliographic record
Abstract
Interest in evaluating conceptual modelling techniques has recently experienced a revival, in part due to widespread adoption of the Unified Modelling Language (UML). In addition, the use of ontology as a framework for evaluating conceptual modelling techniques has gained acceptance. In this paper, we consider implications of applying one aspect of the ontology of Mario Bunge to conceptual modelling. Specifically, conceptual modelling has traditionally failed to provide mechanisms to indicate that some properties of types or classes may be considered dependent on others. This paper presents a theoretical rationale, using Bunge's ontological notion of precedence, for explicitly modelling such dependence in conceptual schema diagrams. We present the design of an experimental framework to test the impact of explicitly representing precedence on the ease with which a diagram can convey domain semantics. In addition, we consider how the issue of common sense semantics can interfere with experimental procedures to evaluate the semantics conveyed in a diagram's structure. We offer early experimental results indicating: 1) the explicit modelling of precedence improves the ability of experimental participants to verify the existence of dependence among properties (but has no effect on the ability to verify the semantics conveyed by association cardinalities); and 2) the potential for background knowledge to interfere with the semantics conveyed by diagram structure. We conclude by discussing the need for further research on both these issues.
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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 | 0.021 | 0.172 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".