Ontology-Based Semantic Search Framework for Disparate Datasets
Bibliographic record
Abstract
The public sector provides open data to create new opportunities, stimulate innovation, and implement new solutions that benefit academia and society.However, open data is usually available in large quantities and often lacks quality, accuracy, and completeness.It may be difficult to find the right data to analyze a target.There are many rich open data repositories, but they are difficult to understand and use because these data can only be used with a complex set of keyword search options, and even then, irrelevant or insufficient data may eventually be retrieved.To alleviate this situation, ontology-based semantic search has been proven to be an effective way to improve the quality of related content queries in such repositories.In this paper, we propose a new method of semantic linking and storing open government datasets of New Zealand's agriculture, land and rainfall sectors based on the use of ontology.The generated ontology can construct integrated data, in which a unified query can be applied to extract richer and more useful information.To validate our model, we showed how to link ontology manually and automatically.Manual linking requires domain experts, and automatic linking reduces the overhead of relying on domain experts to manually link concepts.The results of this method are promising in terms of improving data quality and search efficiency.In future, the proposed model can be integrated with other domain ontologies.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".