Self Managing Top-k (Summary, Keyword) Indexes in XML Retrieval
Bibliographic record
Abstract
Retrieval queries that combine structural constraints with keyword search represent a significant challenge to XML data management systems. Queries are expected to be answered as efficiently and effectively as in traditional keyword search, while satisfying additional constraints. Several XML-retrieval systems support answering queries exhaustively by storing both structural indexes and a keyword index. Other systems answer top-k queries efficiently by constructing indexes in which keyword scores, for some structural elements, are stored in relevance order, enabling approaches such as the threshold algorithm (TA). In this paper we describe TReX an XML retrieval system that can exploit multiple structural summaries (including newly defined ones). TReX can also self-manage small, redundant, indexes to speed up the evaluation of workloads of top-k queries. The redundant indexes are maintained to enable TReX to select an evaluation strategies among three (and potentially more) retrieval methods. We provide experimental evidence that using several strategies improves the efficiency of query evaluation, since none of the retrieval methods outperforms the others in all cases.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".