Forming Cognitive Maps of Ontologies Using Interactive Visualizations
Bibliographic record
Abstract
Ontology datasets, which encode the expert-defined complex objects mapping the entities, relations, and structures of a domain ontology, are increasingly being integrated into the performance of challenging knowledge-based tasks. Yet, it is hard to use ontology datasets within our tasks without first understanding the ontology which it describes. Using visual representation and interaction design, interactive visualization tools can help us learn and develop our understanding of unfamiliar ontologies. After a review of existing tools which visualize ontology datasets, we find that current design practices struggle to support learning tasks when attempting to build understanding of the ontological spaces within ontology datasets. During encounters with unfamiliar spaces, our cognitive processes align with the theoretical framework of cognitive map formation. Furthermore, designing encounters to promote cognitive map formation can improve our performance during learning tasks. In this paper, we examine related work on cognitive load, cognitive map formation, and the use of interactive visualizations during learning tasks. From these findings, we formalize a set of high-level design criteria for visualizing ontology datasets to promote cognitive map formation during learning tasks. We then perform a review of existing tools which visualize ontology datasets and assess their interface design towards their alignment with the cognitive map framework. We then present PRONTOVISE (PRogressive ONTOlogy VISualization Explorer), an interactive visualization tool which applies the high-level criteria within its design. We perform a task-based usage scenario to illustrate the design of PRONTOVISE. We conclude with a discussion of the implications of PRONTOVISE and its use of the criteria towards the design of interactive visualization tools which help us develop understanding of the ontological space within ontology datasets.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".