Instance-Based Ontology Matching For Open and Distance Learning Materials
Bibliographic record
Abstract
<p class="3">The present work describes an original associative model of pattern classification and its application to align different ontologies containing Learning Objects (LOs), which are in turn related to Open and Distance Learning (ODL) educative content. The problem of aligning ontologies is known as Ontology Matching Problem (OMP), whose solution is modeled in this paper as a binary pattern classification problem. The latter problem is then solved through the application of our new proposed associative model. The solution proposed here allows the alignment of two different ontologies —both in the Learning Objects Metadata (LOM) format— into a single ontology of LOs for ODL in LOM format, without redundant objects and with all inherent advantages for handling ODL LOs. The proposed model of pattern classification was validated through experiments, which were done on data taken from the Ontology Alignment Evaluation Initiative (OAEI) 2014 campaign, as well as on data taken from two known educative content repositories: ADRIADNE and MERLOT. The obtained results show a high performance when compared against some of the classifier algorithms present in the state of the art.</p>
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".