Advances in Classification Research Online 2013 Classification, Ontology, and the Semantic Web
Bibliographic record
Abstract
The Semantic Web is developing slowly, but arguably surely. Two inter-related sources of delay are network effects and ontologies. The Semantic Web has come over time to rely onformal ontologies but there are many of these and they are each hard to master. The ability to link databases is compromised by the use of incompatible ontologies. But the RDF triplet format at the centre of the Semantic Web insists only on triplets of the form (object) (predicate orproperty) (subject). This paper explores the potential for a classification system that contains these three types of hierarchies (things, predicates, properties), plus a minimal set of rules on how they can be combined, to serve the needsof the Semantic Web. To this end, it surveys theroles (both the intended roles and side-effects) that formal ontologies play within the Semantic Web. The paper also briefly reviews the challenges faced in applying existing classification systems or thesauri to the Semantic Web.<br />
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.
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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.015 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.012 | 0.024 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.008 |
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".