Ontology Alignment: First Step towards Voice Control for Smart-Home Assistive Scenarios
Bibliographic record
Abstract
With the ageing of the world population, Do-ItYourself approaches and Smart Personal Assistant are combined to provide customised systems that help people in their daily lives, including fostering their self-reliance and promoting ageing in place, even when these people have cognitive disabilities or perceptual impairments such as “Alzheimer” disease. Ambient Assisted Living systems which rely on this combination can also help caregivers reduce their workload by giving them much relevant information about the health status and the situation of the patients at home, and, furthermore, by giving them facilities to create home automation scenarios to assist the patients. A scenario is the arrangement of daily activities in order to achieve a particular goal. In this paper, we propose a new approach to creating assistance scenarios through voice command, involving two types of ontologies, a smart home ontology (OntoDomus) and a knowledge graph of an activity/action extracted from a scenario in the form of structured knowledge. In particular, we leverage ontology alignment to process voice requests in order to create an assistance scenario for the elderly.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".