SE-3D: a controlled comparative usability study of a virtual reality semantic hierarchy explorer
Bibliographic record
Abstract
Keyword searching (e.g., Google or Yahoo!) is based on uncontrolled vocabulary matching which often produces large and noisy result sets. This can waste the time of the searcher who has to sift through long lists of often irrelevant information. The Semantic Web initiative aims to address this issue and includes the description of content using controlled ontologies (i.e., sets of descriptive terms and their relations). Ontologies are partly hierarchical structures too large to display on a single computer screen and thus difficult for searchers to explore efficiently. In an attempt to address these issues, this research has developed and tested Subject Explorer 3D (SE-3D): an information visualization (IV) virtual reality (VR) information retrieval (IR) application based on the metaphor of exploring a physical space. SE-3D aimed to facilitate the visual exploration of information by offering searchers an interactive representation of the subject structure found in the Library of Congress Subject Headings (LCSH). SE-3D is a visual subject ontology navigation tool integrated with keyword searching and relevance ranking of a realworld information collection.SE-3D was tested by 24 undergraduate students during a repeated measures within-subject experiment. As compared with a text-only baseline, SE-3D produced an advantage in accuracy. Participants were more patient with SE-3D, they preferred it and perceived it as more useful. The application used a new technique to manage hundreds of overlapping textual labels in virtual reality, and offered a novel integration of explorative and specific keyword searching. The analysis of the collection revealed that subject assignments followed a power law; the top 1% most assigned subjects contained over 58% of the collection and 65% of non-empty subjects contained a single document.The findings suggest it is possible to extract additional value from organized collections by offering untrained users a reconstructed subject structure integrated with keyword searching. This research is significant for the development and testing of improved bridges between information organization and IR, and interactive information visualization.
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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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".