Constructing a Knowledge Graph from OpenStatistical Data: The Case of Nova ScotiaDisease Datasets
Bibliographic record
Abstract
Abstract The majority of available datasets in open government dataare statistical. They are widely published by various governments to beused by the public and data consumers. However, most open data por-tals do not provide the five-star Linked Data standard datasets. Thepublished datasets are isolated from one another while conceptually con-nected. Through this paper, a knowledge graph is constructed for thedisease-related datasets of a Canadian government data portal, NovaScotia Open Data. We leverage the Semantic Web technologies to trans-form the disease-related datasets into the Resource Description Frame-work (RDF) standard and enrich them with semantic rules. An RDFdata model using the RDF Cube vocabulary is designed in this work todevelop the graph that adheres to best practices and standards, allowingfor expansion, modification and flexible re-use 3. The study also discussesthe lessons learned during the cross-dimensional knowledge graph con-struction and integrating open statistical datasets from multiple sources.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".