Relevancy between Anchor Text and Wikipedia. A Web Search Framework
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.
Bibliographic record
Abstract
The overall volume of data available on the Internet is growing rapidly while finding relevant documents is becoming increasingly difficult. Moreover, queries entered by users are unique, unstructured and often ambiguous while the process has changed dramatically from standard query languages that governed by strict syntax rules to unstructured strings. In Web information retrieval, search paradigms used term occurrences to weight document content prior to any boosting stage. PageRank algorithm, for instance, was used integrated techniques to enhance post retrieval document relevancy to adequately compromise the overall process in two stages. Nevertheless, hypertexts in Web have been used for improving the quality of search results for the most common type of queries. Our main premise is that hypertexts play an important role for ranking documents in IR such as margining between user queries and consensus hypertext. We propose a new algorithm that uses term impact technology for compromising hypertext weighting in Web along with Wikipedia for efficiently find most relevant documents among large set of results. Our experimental results showed that Wikipedia could efficiently improve document relevancy rank when combined with hypertexts for exhibit robust and very good short-term process capability.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it