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 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".