Extracting Data from the Deep Web with Global-as-View Mediators Using Rule-Enriched Semantic Annotations.
Bibliographic record
Abstract
Abstract. The Deep Web offers approximately 500 times more information than the Open Web, but is “hidden ” behind search-forms intended for human users, and typically requires interaction, which makes it difficult to index by Web crawlers. We argue that traditional data extraction is therefore not suitable for the Deep Web and suffers from coverage problems similar to those search engines face when trying to index its content. Instead, it is proposed to trans-form and forward queries on demand using Global-as-View Mediators. To al-low automated interaction with databases on the Deep Web, we use rules that exploit features (e.g. HTML attribute values) to identify elements on a Web page and infer semantic annotations that link these elements to known concepts (e.g. query parameters or result values). Using a prototypical implementation, Deep Web Mediator, the performance of this approach is demonstrated in a classified-advertising use case. Our system is able to answer complex queries by transforming and forwarding them to multiple sites as well as integrating the local results.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| 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".