WebKIV: visualizing structure and navigation for Web mining applications
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
A significant part of the Web mining problem is simply in understanding the value of any mining method. For example, the value of Web mining to improve user navigation is even more challenging if one can't visualize the differences over a large collection of Web pages or a significant structure within the existing Web. We present WebKIV, a tool we've developed to help us visualize our own results in Web mining. WebKIV combines strategies from several other Web visualization tools, to provide a single method of visualizing Web structure, and the results of Web mining on that structure. We summarize the value of Web visualization tools along the dimensions of scale (can one visualize small and large structures), navigation dynamics (can one visualize navigation dynamically or statically), and cumulative usage (can one distinguish individual and aggregate Web usage). We then show how WebKIV provides a way of visualizing the results of Web mining in a way that distinguishes properties along all three of these dimensions.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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