AUTOMATIC ONTOLOGY GENERATION OF BIM AND GIS DATA
Bibliographic record
Abstract
Abstract. Data represented in the form of geospatial context and detailed building information are prominently nurturing infrastructure development and smart city applications. Bringing open-formats from data acquisition level to information engineering accelerates geospatial technologies towards urban sustainability and knowledge-based systems. BIM and GIS technologies are known to excel in this domain. However, fundamental level differences lie among their data-formats, which developed integration methods to bridge the gap between these distinct domains. Several studies have conducted data, process, and application-level integration, considering the significance of collaboration among these information systems. Although integration methods have narrowed the gap of geometric dissimilarity, semantic inconsistency, and information loss yet add constraints towards achieving interoperability. Integration using semantic web technology is more flexible and enables process-level integration without changing data format and structure. However, due to its developing nature and complex BIM-GIS data-formats, most approaches adapted requires human intervention. This paper presents a method, named OGGD (Ontology Generation for Geospatial Data), that implements a formal method for automatic ontology generation from XSD documents using transformation patterns following three extensive processes; first, formalization of XSD elements and transformation patterns; the second process identifies corresponding patterns explicitly, and the last process generates ontology for XSD schema. XSD elements from open-standard data models of BIM and GIS, ifcXML and CityGML, are manipulated and transformed into a semantically rich OWL model. The ontology models created can be applicable for information-based integration systems that will nurture knowledge-discovery and urban applications.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".