Creating Proprietary Terms Using Lightweight Ontology: A Case Study on Acquisition Phase in a Cyber Forensic Process.
Why this work is in the frame
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Bibliographic record
Abstract
Terms and their meaning connections provided by the Resource Description Framework (RDF) present nowadays the standard mechanism for Linking Data (LD) on the web. All the existing terms, whether they are built-in terms (imported from well-known vocabularies on the semantic web) or proprietary terms (custom terms created by data publisher) can be used to describe and link different things in the world through RDF statements, and by applying the general architecture of the World Wide Web known as Linked Data Principles (LDP). Sometimes, these existing terms are not enough and adequate to describe a particular data set; more proprietary terms need to be created and developed in a dedicated vocabulary using lightweight ontology of LD. The latter uses the constructors of Resource Description Framework Schema (RDFS) and little features from Web Ontology Language (OWL) to create new proprietary terms describing such data set. This idea is depicted in this paper through a phase retrieved from a Cyber Forensic (CF) process, called acquisition phase, where different forensic tasks need to be described using new proprietary terms. This paper explains how these new proprietary terms can be created and published using the constructors of the lightweight ontology to describe this forensic phase. Keywords—Linked Data; Linked Data Principles; Resource Description Framework Schemas; Web Ontology Language; Proprietary Terms; Cyber Forensic; State Preservation.
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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 | 0.000 | 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 it