3D Virtual World Retrieval Based on Ontology and Content
Bibliographic record
Abstract
On the Web and on informatics systems, 3D virtual worlds used become big both in number and in size. Therefore, we propose in this paper a new method for retrieving 3D virtual worlds based on the semantics and the content. Firstly, we propose a new classified database of 3D virtual worlds given in VRML format. To achieve the semantic method, we construct an ontology that describes virtual worlds in various aspects including their contents (3D objects building a virtual world) and information about their contents (authors, file format, etc). This ontology is presented by OWL, the W3C recommended language. So as to extract desired 3D scenes from the proposed database we use the SPARQL query language. We propose finally a shape based method for searching desired virtual worlds by content in this database. This method is based on a new distance (metric) that define the similarity between virtual worlds. This method is evaluated using the recall vs. precision curves.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".