Ontology Based Object-Oriented Domain 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
An important step in developing the requirements for an information system is analyzing the application domain. In this step, conceptual models are used for representing an application domain. However, while languages for software design are available and widely used, no generally accepted language exists for conceptual modeling. This work suggests the use of object-oriented software modeling languages also for conceptual modeling. Such use can support a more accurate transition from domain models to software models. As software-modeling languages were not intended for modeling application domains, their constructs lack the required semantics. While previous papers addressed the representation of structural elements of domains using object concepts, this paper addresses behavioral aspects, related to change and interaction. The proposed semantics are based on a mapping between ontological concepts that describe behavior and object-oriented constructs related to dynamics. Based on these mappings, modeling rules are proposed to guide the modeler in creating ontologically well-formed models. The mappings and rules are exemplified using UML and are demonstrated using a case study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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