Guidelines for Designing Visual Ontologies to Support Knowledge Identification1
Bibliographic record
Abstract
Organizations often provide workers with knowledge management systems to help them obtain knowledge they need. A significant constraint on the effectiveness of such systems is that they assume workers know what knowledge they need (they know what they don’t know) when, in fact, they often do not know what knowledge they need (they don’t know what they don’t know). A way to overcome this problem is to use visual ontologies to help users learn relevant concepts and relationships in the knowledge domain, enabling them to search the knowledge base in a more educated manner. However, no guidelines exist for designing such ontologies. To fill this gap, we draw on theories of philosophical ontology and cognition to propose guidelines for designing visual ontologies for knowledge identification. We conducted three experiments to compare the effectiveness of guided ontologies, visual ontologies that followed our guidelines, to unguided ontologies, visual ontologies that violated our guidelines. We found that subjects performed considerably better with the guided ontologies, and that subjects could perceive the benefits of using guided ontologies, at least in some circumstances. On the basis of these results, we conclude that the way visual ontologies are presented makes a difference in knowledge identification and that theories of philosophical ontology and cognition can guide the construction of more effective visual representations. Furthermore, we propose that the principles we used to create the guided visual ontologies can be generalized for other cases where visual models are used to inform users about application domains.
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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 | 0.032 | 0.092 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.007 | 0.017 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".