Multilingual Ontology Specification: a collaborative approach
Bibliographic record
Abstract
In this paper we present a method to support the process of multilingual ontology specification developed during the conceptualization phase in the context of multilingual collaborative networks. This method was developed to answer the requirements of a collaborative network where the need for localized content appears at an earlier stage, due to the short life-cycle that characterizes both this type of network. We present the main results obtained during the method's implementation in the context of an European project - H-Know: Advanced Infrastructure for Knowledge Based Services for Restoring Buildings, analyse its problems and limitations and discuss the difficulties that arise in a shared conceptualization of an ontology represented in more than one natural language from the point of view of the language influence, taking into account aspects such as the process of interpretation and representation of knowledge of a specific subject field, the needs and difficulties when accessi
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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 | 0.021 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".