A multi-matching technique for combining similarity measures in ontology integration
Bibliographic record
Abstract
Ontology matching is a challenging problem in many applications, and is a major issue for interoperability in information systems. It aims to find semantic correspondences between a pair of input ontologies, which remains a labor intensive and expensive task. This thesis investigates the problem of ontology matching in both theoretical and practical aspects and proposes a solution methodology, called multi-matching . The methodology is validated using standard benchmark data and its performance is compared with available matching tools. The proposed methodology provides a framework for users to apply different individual matching techniques. It then proceeds with searching and combining the match results to provide a desired match result in reasonable time. In addition to existing applications for ontology matching such as ontology engineering, ontology integration, and exploiting the semantic web, the thesis proposes a new approach for ontology integration as a backbone application for the proposed matching techniques. In terms of theoretical contributions, we introduce new search strategies and propose a structure similarity measure to match structures of ontologies. In terms of practical contribution, we developed a research prototype, called MLMAR - Multi-Level Matching Algorithm with Recommendation analysis technique, which implements the proposed multi-level matching technique, and applies heuristics as optimization techniques. Experimental results show practical merits and usefulness of MLMAR
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 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".