How to quickly detect the overlap and the consistency between LADM with LandInfra and LandXML: Application of schema matching techniques
Bibliographic record
Abstract
In this paper, we explore the schema matching techniques to compare the content of three geospatial standards which are LADM, LandInfra (InfraGML) and LandXML. Those standards all refer to the concept of “land” and we will try to quantify the similarity of them based on syntax and semantic comparison of the class names exposed in their respective schema. Consequently, we will demonstrate the applicability, the accuracy and the usefulness (rapidity and automation) of schema matching techniques for comparing the content of standards. The comparison is performed with XSD (XML Schema Definition) files that describe the schema in English. The results show that syntactic match rate between LADM-LandInfra (54%) is higher than LADM-LandXML (10%). In adding the semantic information extracted from Wordnet, the match rate between LADM-LandInfra goes to 84% and 59% for LADM-LandXML. In comparing our matching results with two independent sources of information that already and manually compared these three standards, we obtained distinctive results. The correctness of LADM-LandInfra is 60%, while the correctness of LADM-LandXML is only 20%. The applicability of schema matching is positively demonstrated while the usefulness and the accuracy still need further improvements in order to make any statement.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".