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Record W1928525277 · doi:10.6082/r335k-e3h07

Exploratory Search Interfaces for the UNESCO Multilingual Digital Library: Combining Visualization and Semantics

2011· article· en· W1928525277 on OpenAlexaff
Stan Ruecker, Ali Shiri, Carlos Fiorentino, Mark Bieber

Bibliographic record

VenueKnowledge@UChicago (University of Chicago) · 2011
Typearticle
Languageen
FieldComputer Science
TopicSemantic Web and Ontologies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsComputer scienceThesaurusVisualizationSemantics (computer science)Interface (matter)Information retrievalDigital libraryWorld Wide WebSpace (punctuation)Tag cloudUser interfaceHuman–computer interactionNatural language processingArtificial intelligenceLinguistics

Abstract

fetched live from OpenAlex

The objective of this paper is to report on the design of semantically–rich and dynamic visual user interfaces to support exploratory interaction with the UNESCO digital library materials. The UNESCO multilingual thesaurus, which supports English, French, and Spanish, has been utilized to provide a semantic and multilingual environment where users can browse the thesaurus using word buckets, formulate queries, explore the conceptual space of their query terms and view results in a dynamic and highly interactive environment. The interface design focuses on integrating searching and browsing and multilingual elements in a visual environment where terms and retrieved documents are designed as visual objects. To facilitate users' interaction with terms, thesauri relationships are shown using such elements as colour, size, location, and distance.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0040.004
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.047
GPT teacher head0.248
Teacher spread0.201 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2011
Admission routes1
Has abstractyes

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