An empirical comparison of ontology matching techniques
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Ontology matching aims to find semantic correspondences between a pair of input ontologies. A number of matching techniques have been proposed recently. We may, however, benefit more from a combination of such techniques as opposed to just a single method. This is more appropriate, but very often the user has no prior knowledge about which technique is more suitable for the task at hand, and it remains a labour intensive and expensive task to perform. Further, the complexity of the matching process as well as the quality of the result is affected by the choice of the applied matching techniques. We study this problem and propose a framework for finding suitable matches. A main feature of this is that it improves the structure matching techniques and the end result accordingly. We have developed a running prototype of the proposed framework and conducted experiments to compare our results with existing techniques. While being comparable in efficiency, the experimental results indicate our proposed technique produces better quality matches.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.008 |
| Open science | 0.001 | 0.000 |
| 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 it