Joint Proceedings of the Onto4FAIR 2023 Workshops:collocated with 13th International Conference on Formal Ontology in Information Systems, FOIS 2023, Sherbrooke, QC, Canada, July 20, 2023 and 19th International Conference on Semantic Systems, SEMANTICS 2023
Bibliographic record
Abstract
Semantic interoperability is crucial for the FAIR Principles and strongly relies on Semantic Artefacts that also need to be FAIR.To achieve this, semantic artefacts require rich, structured, and interoperable metadata.The challenge lies in determining the threshold for "rich metadata" and agreeing on a common minimum set.The H2020 FAIRsFAIR project and the RDA Vocabulary Semantic Services Interest Group addressed this question by developing a "minimal metadata model" for semantic artefacts.In this paper, we present background information, methodology, discussions and workshops which contribute to the establishment of the FAIRsFAIR minimum metadata profile for semantic artefacts.We present an extension of the Metadata for Ontology Description and Publication Ontology (MOD2.0)incorporating this profile as well as its implementation (SemanticDCAT-AP) and its use to build FAIRcat, a prototype of a FAIR Data Point harvesting the content of multiple semantic artefact catalogues.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".