EXPLORING SCHEMA MATCHING TO COMPARE GEOSPATIAL STANDARDS: APPLICATION TO UNDERGROUND UTILITY NETWORKS
Bibliographic record
Abstract
Abstract. This paper proposes a preliminary analysis of whether a schema matching approach can be applied for the comparison and possible the selection of geospatial standards. Schema matching is tested in the context of underground utility network modelling and, as an initial experiment, three geospatial standards are compared with user requirements: CityGML UtilityNetwork ADE, infraGML and IFC. The schema comparison is enabled by XSD files, and carried out from syntactic, structural and semantic points of view, making use of existing software. The findings of this preliminary investigation show that schema matching is applicable for the comparison of user needs and existing geospatial standards, and does show some potential, but the matching results are varied and not easy to interpret. In particular, the similarity scores between user needs and standards are very low and the comparison and the selection is not straightforward. Having a strategy - an iterative process - is required. While for this preliminary examination, the focus of this paper is on assessing the schema matching approach (which parameters to take into consideration, how to proceed, tools available, automation aspect), further work will include examining software options and performance, as well as exploring how to take the relatively complex preliminary results obtained here and use them to assist the selection of a specific standard.
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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 | 0.027 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".