Validating the OntoLex-lemon lexicography module with K Dictionaries' multilingual data
Bibliographic record
Abstract
The OntoLex-lemon model has gradually acquired the status of de-facto standard for the representation of lexical information according to the principles of Linked Data (LD). Exposing the content of lexicographic resources as LD brings both benefits for their easier sharing, discovery, reusability and enrichment at a Web scale as well as for their internal linking and better reuse of their components. However, with lemon being originally devised for the lexicalization of ontologies, a 1:1 mapping between its elements and those of a lexicographic resource is not always attainable. In this paper we report our experience of validating the new lexicog module of OntoLex-lemon, which aims at paving the way to bridge those gaps. To that end, we have applied the module to represent lexicographic data coming from the Global multilingual series of K Dictionaries (KD) as a real use case scenario of this module. Attention is drawn to the structures and annotations that lead to modelling challenges, the ways the lexicog module tackles them, and where this modelling phase stands as regards the conversion process and design decisions for KD’s Global series. Keywords: Linguistic Linked Data; RDF; multilingual; OntoLex-lemon; K Dictionaries
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".