OntoCin: A Multimedia Ontology for the Semantic Indexation of Cinematographic Resources on the Web of Data
Bibliographic record
Abstract
The flood of Multimedia Resources on the Web of Data and offline platforms is a clear proof of the increase of such resources in day-to-day activities of modern society, especially web series, documentaries, fictions, etc. Multimedia Ontologies applied to film-related features entail to describe not only the film production process, but also their social and environmental inferences (with cultures, attractive sites, etc.).The insufficient insight of annotated features in existing ontologies affects retrieval accuracy on useful facts necessary in today's society.This paper presents a Multimedia Ontology for the co-construction and indexing of Cinematographic resources (OntoCin) on the Semantic Web, built on the Human Activity Theory (HAT) modelling approach and the Competency-based Questions Methodology which allowed to scope cinematographic knowledge.This ontology enables information retrieval by enhancing annotation and indexing of scenes, emotions, shooting places (touristic sites), film-users' preferences, socio-cultural knowledge and impressions on the Web of Data.We made some queries on the ontology and came out with results.This helped to set ground for a semantic wiki architecture that will facilitate the co-construction of multimedia resources based on this Multimedia Ontology.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".