Privacy Agents and Ontology for the Semantic Web
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Conducting international e-business requires knowledge about the privacy and consumer protection laws, and regulations that affect transacting parties. To support this requirement, we describe a high-level organization for a Web-privacy ontology composed of a hierarchical organization of formal laws and acts, informal cultural guidelines and standards for business in general, and specific legislation and guidelines for interest groups. A prototype of a Web privacy ontology is built for the Canadian Personal Information Protection and Electronic Documents Act (PIPEDA). The ontology is built upon XML infrastructure. We propose that existing and to-be-designed XML-based P3P tags be incorporated in any regulatory privacy ontology to enable future P3P agents to automatically match not only the business’ privacy policy with the user preference rules, but also to match the business privacy policy with the privacy laws (for a start), and later with commerce and othertype laws, applicable to the stated legal jurisdiction(s) in which the business operates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 it