How to quickly detect the overlap and the consistency between LADM with LandInfra and LandXML: Application of schema matching techniques
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper, we explore the schema matching techniques to compare the content of three \ngeospatial standards which are LADM, LandInfra (InfraGML) and LandXML. Those standards \nall refer to the concept of “land” and we will try to quantify the similarity of them based on \nsyntax and semantic comparison of the class names exposed in their respective schema. \nConsequently, we will demonstrate the applicability, the accuracy and the usefulness (rapidity \nand automation) of schema matching techniques for comparing the content of standards. The \ncomparison is performed with XSD (XML Schema Definition) files that describe the schema \nin English. The results show that syntactic match rate between LADM-LandInfra (54%) is \nhigher than LADM-LandXML (10%). In adding the semantic information extracted from \nWordnet, the match rate between LADM-LandInfra goes to 84% and 59% for LADMLandXML. In comparing our matching results with two independent sources of information \nthat already and manually compared these three standards, we obtained distinctive results. The \ncorrectness of LADM-LandInfra is 60%, while the correctness of LADM-LandXML is only \n20%. The applicability of schema matching is positively demonstrated while the usefulness and \nthe accuracy still need further improvements in order to make any statement.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it