UFleSe: User-Friendly Parametric Framework for Expressive Flexible Searches
Bibliographic record
Abstract
We present a parametric framework (UFleSe) with a user-friendly interface having a search engine that enables regular users (without the need of neither technical nor theoretical knowledge) to define their fuzzy concepts, rules, similarity relations, synonyms, antonyms, and personalizing their definitions for different users, and to link them with the crisp database fields for performing flexible, expressive queries in a language close to natural language. It works over multiple modern and conventional data formats, such as JSON, SQL, Prolog, CSV, XLS, and XLSX. We present the syntax involved in the construction of our various flexible searching criteria and their personalizations. Furthermore, we present the architecture of this novel system that combines fuzzy, crisp data, and similarity relations in its queries to return constructive answers ordered by a degree of searching criteria satisfaction (truth-value between 0 and 1). Finally, we include a comparative analysis of different fuzzy querying systems here, and we provide various experiments, to show the system behavior, performance, efficiency, and scalability as well.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.008 |
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".