Improving the Effectiveness of XML Retrieval with User Navigation Models
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
Structured documents (predominantly encoded in XML) utilize markup dialects for several purposes, such as conveying logical structure, or providing rendering instructions. XML structure can also help users to navigate within documents to satisfy their information needs. However, including the user's structural preferences in the ranking of retrieved elements remains a key challenge in XML retrieval. In this paper, we propose an approach for including structural preferences in the ranking of XML elements by improving the structural relevance (SR) of results. SR is an evaluation measure which relies on graphical navigation models to capture the structural preferences of users. We propose several algorithms to post-process search engine output to improve the SR of the output. Experimental results (using data, assessments, and search engines from INEX 2007 and 2008) demonstrate the effect of different combinations of post-processing algorithms and navigation models on the effectiveness of systems.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 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