Towards understanding the needs of cognitive support
Bibliographic record
Abstract
Researchers have developed a large number of ontology-mapping algorithms in recent years. However, ontology mapping is hardly a fully automated task and users must verify and fine-tune the mappings resulting from automated algorithms. Both academic and industry researchers have focused on the algorithms themselves and largely ignored the issue of cognitive support for users in the task of analyzing mappings proposed by the algorithms and creating new mappings. The lack of comprehensive user-oriented tools for ontology mapping (rather than just algorithms) hinders the adoption of the new technologie. In this paper, we analyze requirements for cognitive support for the ontology-mapping task. Recognizing that many researchers must focus on improving the algorithm performance itself (or only on providing better visualization), we have developed a plugin framework that enables developers to assemble a comprehensive ontology-mapping tool by plugging in various components. We provide a reference implementation of the complete framework. Thus, developers can plug in only the components they are interested in. For example, algorithm developers can plug in their algorithm and use the visualization components that we provide and the user-interface researchers can use the framework to experiment with various visualization paradigms for ontology mapping (and not worry about implementing the algorithms themselves). We also discuss specific cognitive aids for ontology mapping that we have developed and that are available as part of this framework.
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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 | 0.011 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.014 | 0.033 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".