Linking relational concept analysis and variability model within context modeling of context-aware applications
Bibliographic record
Abstract
The development of context-aware applications is a complex process that involves context management. A context life cycle implies 4 essential steps: context acquisition, context modeling, context reasoning and context dissemination [1]. In [2], we tackled the issue of context modeling and reasoning by proposing an approach based on Relational Concept Analysis (RCA) and Descriptive Logic (DL), respectively. A main aspect that is neglected in context model is the context variability, which we refer to it as a range or set of context values of an environment along which that environment changes. Context variability can benefit from software product lines [3]. For this reason, this paper proposes an approach describing the relationship between a context RCA-based model and a feature model in order to describe the variability of contexts in which software of a software product line is used. Thus, we represent a feature model as ontology to obtain a semantic model. We also define context rules derived from the context model and based on SWRL that we apply to SPL configurations. We explain the inferred reasoning justifications obtained from a reasoner and detail the implementation of our proposed approach.
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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 | 0.001 | 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 | 0.000 | 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".