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Record W2070694625 · doi:10.1002/bult.2012.1720380410

Interactive visualization for multilingual search

2012· article· en· W2070694625 on OpenAlexaff
Stan Ruecker, Ali Shiri, Carlos Fiorentino

Bibliographic record

VenueBulletin of the American Society for Information Science and Technology · 2012
Typearticle
Languageen
FieldComputer Science
TopicSemantic Web and Ontologies
Canadian institutionsMacEwan UniversityUniversity of Alberta
Fundersnot available
KeywordsComputer scienceVisualizationInterface (matter)Information retrievalContext (archaeology)Human–computer interactionUser interfaceInformation visualizationWorld Wide WebInteractive visualizationVisual searchArtificial intelligenceProgramming language

Abstract

fetched live from OpenAlex

Abstract Editor's Summary Multilingual thesauri provide the context for exploring experimental user interfaces that support interactive visualization. The authors created two search interfaces that draw on the semantic richness of bilingual thesauri and provide for search, browse and results display. The Searchling interface offers search, browsable navigation and the full term‐record data for a selected term, as well as a simple switch between language views. It provides active user assistance by displaying options to broaden or narrow search. User evaluation showed Searchling to be easy to use, appealing broadly but especially to linear thinkers. The T‐Saurus interface is more visual, with term search results represented by the size, number, proximity and opacity of buckets. Terms can be displayed in multiple languages simultaneously, and multiple terms can be chosen to form a query to retrieve documents. Visual thinkers appreciated its dynamic and interactive visualization interface. Studies demonstrate the support both interfaces provide for fully using rich, multilingual thesauri as well as differences for users with different cognitive styles.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.531
Threshold uncertainty score0.474

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.319
Teacher spread0.300 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2012
Admission routes1
Has abstractyes

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