XML Schema Integration to Facilitate E-Commerce
Bibliographic record
Abstract
XML has become the de facto standard for Information Exchange protocol for e-commerce and many workgroup applications such as Enterprise Resource Planning (ERP). The availability of large amounts of heterogeneous distributed web data necessitates the integration of XML data from multiple XML sources for many reasons. Currently, there are many e-commerce companies, which sell similar products but represent them using different XML schemas with possibly different ontologies. When any two such companies merge, there is a need for a uniform schema integration methodology. In some applications like comparison-shopping, there is a need for an illusionary centralized homogeneous information system. In this chapter, we propose an XML Schema integration methodology. We define an object-oriented data model called XSDM (XML Schema Data Model) and present a graphical representation of XML Schema for the purpose of schema integration. We use a three-layered architecture for XML Schema integration, with each layer presenting an integrated view of the concepts that characterize the layer below. The three layers included are namely pre-integration, comparison and integration. During pre-integration, an analysis of the schemas to be integrated occurs. During the comparison phase of integration, correspondences as well as conflicts between elements are identified. During the integration phase, restructuring and merging of the initial schemas takes place to obtain the global schema. We define integration policies for integrating element definitions as well as their data types and attributes. The policies are also applicable in integrating DTD schemas with other DTD/XML Schemas.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".