Searching the hypermedia Web: improved topic distillation through network analytic relevance ranking
Bibliographic record
Abstract
The Web is a large hypermedia space that is generally explored using search engines. These search engines are evolving to make more effective use of the hypermedia structure of the Web. This paper contributes to this evolution by proposing new methods of topic distillation in structured search based on co-citation and network analysis. We describe a set of 21 network analysis measures of relevance in Web search output. These measures are then compared with human judgments in two studies. In the first study, we compare the average judged relevance of the top 20 search results selected by Google vs. the top 20 results as selected by each of the 21 network analysis measures. All but one of the network analysis measures (“inlink”) showed significantly (p<.05) better (as compared to Google) average judged relevance amongst their top 20 selections. Stepwise regression analysis was then used to identify a linear model with three network analysis measures as predictors, which accounted for roughly 17% of the variance in relevance judgments. In the second study the human judges compared ranked output from Google with the ranked output from the best fitting one- and three-predictor regression models. There was a tendency for people to prefer the ranked output from the three-predictor regression model. Only four of the 21 subjects made the Google output their first choice (out of the three options given to them). The output as ranked by the three-predictor model was also rated as having (within the top 20 ranked results) significantly more highly relevant results, and significantly fewer irrelevant results, than the corresponding ratings for Google. While these results need to be extended with more detailed analysis of a wide range of queries and topics, they suggest that network analysis of search output adjacency matrices (where adjacency/proximity is based on Web-wide co-citations) may significantly improve topic distillation by search engines.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".