Bibliographic record
Abstract
XML and its schema language mainly built to encode documents electronically are widely used for a variety of purposes in modern programming. XML Schema is developed in the context of a domain specific terminology. Schema mapping describes the semantic correspondences between the components of two XML Schema documents. An XML Schema document describes the information that resides in an XML document using the XML Schema Definition Language (XSD). Therefore, an XML Schema document should be parsed differently than an XML document, in order to extract the proper information from it. This paper introduces a new solution for the XML Schema matching and mapping problem consisting of a novel approach to the internal representation of XML Schema documents during the matching process. The solution is based on the XML object model. The use of an object model for the XML Schema document representation within the mapping and matching software tool produces the following benefits: (1) the XML Schema document internal representation conforms to the XML Schema Definition Language standard specifications, (2) a better scalability of the software tool, (3) the matching engine is developed based on object-oriented design patterns such as the Composite and the Chain of Responsibility, (4) flexibility of the matching engine and (5) tractability of very complex XML Schema documents. The given examples illustrate plainly the new method presented in this paper.
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.
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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.009 |
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".