MétaCan
Menu
Back to cohort
Record W1489499991

Personalizing XML text search in PIMENT

2005· article· en· W1489499991 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueVery Large Data Bases · 2005
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Database Systems and Queries
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputer scienceInformation retrievalWeb search queryRanking (information retrieval)PersonalizationQuery expansionWeb query classificationXMLWorld Wide WebSearch engine
DOInot available

Abstract

fetched live from OpenAlex

A growing number of text-rich XML repositories are being made available. As a result, more efforts have been deployed to provide XML full-text search that combines querying structure with complex conditions on text ranging from simple keyword search to sophisticated proximity search composed with stemming and thesaurus. However, one of the key challenges in full-text search is to match users' expectations and determine the most relevant answers to a full-text query. In this context, we propose query personalization as a way to take user profiles into account in order to customize query answers based on individual users' needs.We present PIMENT, a system that enables query personalization by query rewriting and answer ranking. PIMENT is composed of a profile repository that stores user profiles, a query customizer that rewrites user queries based on user profiles and, a ranking module to rank query answers.

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.

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)0.000
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: Methods · Consensus signal: none
Teacher disagreement score0.756
Threshold uncertainty score0.460

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.003
Open science0.0010.001
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 head0.056
GPT teacher head0.314
Teacher spread0.258 · 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