Light-Weight Ontology Alignment using Best-Match Clone Detection
Bibliographic record
Abstract
Abstract—Ontologies are a key component of the Semantic Web, providing a common basis for representing and exchanging domain meaning in web documents and resources. Ontology alignment is the problem of relating the elements of two formal ontologies for a semantic domain, in order to identify common concepts and relationships represented using different terminology or language, and thus allow meaningful communication and exchange of documents and resources represented using different ontologies for the same domain. Many algorithms have been proposed for ontology alignment, each with their own strengths and weaknesses. The problem is in many ways similar to nearmiss clone detection: while much of the description of concepts in two ontologies may be similar, there can be differences in structure or vocabulary that make similarity detection challenging. Based on our previous work extending clone detection to modelling languages such as WSDL using contextualization, in this work we apply near-miss clone detection to the problem of ontology alignment, and use the new notion of ”best-match ” clone detection to achieve results similar to many existing ontology alignment algorithms when applied to standard benchmarks. Index Terms—Clone detection techniques; OWL; ontology matching; ontology alignment I.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".