XPlainer: Visual Explanations of XPath Queries
Bibliographic record
Abstract
The popularity of XML has motivated the development of novel XML processing tools many of which embed the XPath language for XML querying, transformation, constraint specification, etc. XPath developers (as well as less technical users) have access to commercial tools to help them use the language effectively. Example tools include debuggers that return the result of XPath subexpressions visualized in the context of the input XML document. This paper introduces XPlainer, a language that provides explanations of why XPath expressions return a specific answer. An explanation returns precisely the nodes in the input XML document that contribute to the answer. We provide a complete formalization for explanation queries based on the semantics of XPath. This enables the use of XPath engines for the evaluation of explanation queries. We describe a tool that uses XPlainer queries to provide visual explanations. The XPlainer-Eclipse tool is built on an extensible development environment that includes editors for visualizing both XML documents and XPath expressions as trees together with the explanation of the answers.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.005 |
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".