Advanced Web search based on formal concept analysis.
Bibliographic record
Abstract
Explosive increase of global wide web using has led to "web searches" as one of the most important tasks in our daily lives. However, most web users discover that only a few results are valuable among thousands of results returned. The purpose of this thesis is to derive a new and improved way of web searching. To that end, research aimed at solving the above mentioned problems and surveying the methodologies or approaches adapted to them was found first. Some research papers give hints that help analyze user requirements for web searches. This survey on background research papers and user requirement analyses is one basis of my thesis. Another research area related to Formal Concept Analysis that is the theoretical background of my thesis is also surveyed. Following the research papers surveyed, a new searching methodology, the advanced web searching methodology based on formal concept analysis, is proposed. A lattice derived from the Formal Concept Analysis gives dynamic and interactive aspects to the new niche search engine. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2001 .K56. Source: Masters Abstracts International, Volume: 40-03, page: 0724. Adviser: Young-Gil Park. Thesis (M.Sc.)--University of Windsor (Canada), 2001.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".