Ontology Extraction Tools: An Empirical Study with Educators
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
Recent research in Technology-Enhanced Learning (TEL) demonstrated several important benefits that semantic technologies can bring to the TEL domain. An underlying assumption for most of these research efforts is the existence of a domain ontology. The second unspoken assumption follows that educators will build domain ontologies for their courses. However, ontologies are hard to build, especially for not-tech-savvy users. Tools for ontology extraction from text aim to overcome this problem. We have conducted an empirical study with educators, both from Information Technology (IT) and non-IT domains, where they used current ontology extraction tools to build domain ontologies for their courses from their existing course material. Based on the obtained study results we have drawn conclusions about the existing ontology extraction tools and provided recommendations for their future development so that they can be beneficial for the TEL domain.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 it