Context-Aware Multi-layered Ontology for Composite Situation Model in Pervasive Computing
Bibliographic record
Abstract
With the rapid advancement of technologies and analysis tools in the smart systems, enabling real-time context monitoring of user's living conditions and quality services delivery is increasing. Current studies in this area are focused on developing mobile applications with specific services, based on toolkit that allow developers to obtain context information from sensors. However, there exists a notable lack of ontology able to represent all the necessary context information starting from distributed users, and constantly changing environment. The modeling of user’s domains to represent diverse mobile and IoT devices, and finalizing with the description of user’s composite situations in smart-*(health, home, cities, car, office, etc.) domains. Considering interoperability, reusability, and flexibility, a new context composite situation ontology for smart systems is proposed with better representation of heterogeneous context. The ontology enables to sense, reason, and infer composite situations in various smart domains, prioritizes critical situations and facilitates the delivery of smart mobile service. Proposed ontology is formalized and validated on different smart environments with different user’s situations. Several experiments were carried out with a real-life motivating scenario. Experimental results showed that the proposed approach has reduced queries times and improved flexibility.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| 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".