An automated ontology generation technique for an emergent behavior detection system
Bibliographic record
Abstract
Due to the lack of central control in distributed systems, design and implementation of such systems is a challenging task. Interaction of multiple autonomous components can easily result in unwanted behavior in the system. Therefore it is vital to carefully review the design of distributed systems. Manual review of software documents is too inefficient and error prone. It would therefore be beneficial to have a systematic methodology to automatically analyze software requirements and design documents. However automating the process of software analysis is a challenging task because besides the design know-how, each software system requires its own domain knowledge. Existing approaches often require a great deal of input from system engineers familiar with the domain. Such information needs to be interpreted by the designer which is a time-consuming and error prone process. This research suggests the use of a scenario-based approach to represent system requirements. Scenarios are often depicted using message sequence charts (MSCs). Due to their formal notation, MSCs can be used to analyze software requirements in a systematic manner. In an earlier paper, it was demonstrated that ontologies can be used to effectively automate the construction of domain knowledge for the system. However the construction of ontologies remained a challenging task. This paper describes a process which infers ontology from the provided message sequence charts. Furthermore this paper introduces a software tool which automates the process of domain ontology construction. This methodology is demonstrated using a case study of a fleet-management software system.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".